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\n \n 2022\n \n \n (1)\n \n \n

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\n \n\n \n \n \n \n \n Learning-based non-rigid video depth estimation using invariants to generalized bas-relief transformations.\n \n \n \n\n\n \n Pedone, M.; Mostafa, A.; and Heikkilä, J.\n\n\n \n\n\n\n Journal of Mathematical Imaging and Vision. 2022.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{\n title = {Learning-based non-rigid video depth estimation using invariants to generalized bas-relief transformations},\n type = {article},\n year = {2022},\n id = {851932a9-0e3f-3f32-b997-251336adc2fe},\n created = {2022-06-10T06:09:17.781Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2022-06-10T06:09:17.781Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Pedone, Matteo and Mostafa, Abdelrahman and Heikkilä, Janne},\n journal = {Journal of Mathematical Imaging and Vision}\n}

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\n \n 2021\n \n \n (3)\n \n \n

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\n \n\n \n \n \n \n \n \n Boosting Monocular Depth Estimation with Lightweight 3D Point Fusion.\n \n \n \n \n\n\n \n Huynh, L.; Nguyen, P.; Matas, J.; Rahtu, E.; and Heikkila, J.\n\n\n \n\n\n\n In 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pages 12747-12756, 10 2021. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"Boosting$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Boosting Monocular Depth Estimation with Lightweight 3D Point Fusion},\n type = {inproceedings},\n year = {2021},\n pages = {12747-12756},\n websites = {https://ieeexplore.ieee.org/document/9710583/},\n month = {10},\n publisher = {IEEE},\n id = {25bad72a-4339-30fc-aee5-af7254ba442a},\n created = {2022-06-10T06:09:17.808Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2022-06-10T06:09:17.808Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Huynh, Lam and Nguyen, Phong and Matas, Jiri and Rahtu, Esa and Heikkila, Janne},\n doi = {10.1109/ICCV48922.2021.01253},\n booktitle = {2021 IEEE/CVF International Conference on Computer Vision (ICCV)}\n}

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\n \n\n \n \n \n \n \n \n Sequential View Synthesis with Transformer.\n \n \n \n \n\n\n \n Nguyen-Ha, P.; Huynh, L.; Rahtu, E.; and Heikkilä, J.\n\n\n \n\n\n\n Volume 12625 LNCS . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pages 695-711. 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Lecture$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

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@inbook{\n type = {inbook},\n year = {2021},\n keywords = {Multi-view attention,Sequential view synthesis,Transformer},\n pages = {695-711},\n volume = {12625 LNCS},\n websites = {http://link.springer.com/10.1007/978-3-030-69538-5_42},\n id = {5db13315-c7b3-39ab-b18d-d770687f9e58},\n created = {2022-06-10T06:09:17.810Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2022-06-10T06:09:17.810Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {This paper addresses the problem of novel view synthesis by means of neural rendering, where we are interested in predicting the novel view at an arbitrary camera pose based on a given set of input images from other viewpoints. Using the known query pose and input poses, we create an ordered set of observations that leads to the target view. Thus, the problem of single novel view synthesis is reformulated as a sequential view prediction task. In this paper, the proposed Transformer-based Generative Query Network (T-GQN) extends the neural-rendering methods by adding two new concepts. First, we use multi-view attention learning between context images to obtain multiple implicit scene representations. Second, we introduce a sequential rendering decoder to predict an image sequence, including the target view, based on the learned representations. Finally, we evaluate our model on various challenging datasets and demonstrate that our model not only gives consistent predictions but also doesn’t require any retraining for finetuning.},\n bibtype = {inbook},\n author = {Nguyen-Ha, Phong and Huynh, Lam and Rahtu, Esa and Heikkilä, Janne},\n doi = {10.1007/978-3-030-69538-5_42},\n chapter = {Sequential View Synthesis with Transformer},\n title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}\n}

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\n \n\n \n \n \n \n \n \n RGBD-Net: Predicting Color and Depth Images for Novel Views Synthesis.\n \n \n \n \n\n\n \n Nguyen, P.; Karnewar, A.; Huynh, L.; Rahtu, E.; Matas, J.; and Heikkila, J.\n\n\n \n\n\n\n In 2021 International Conference on 3D Vision (3DV), pages 1095-1105, 12 2021. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"RGBD-Net:$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {RGBD-Net: Predicting Color and Depth Images for Novel Views Synthesis},\n type = {inproceedings},\n year = {2021},\n pages = {1095-1105},\n websites = {https://ieeexplore.ieee.org/document/9665841/},\n month = {12},\n publisher = {IEEE},\n id = {6fdcadf6-cd99-3f23-93c8-9b506a8c7ab6},\n created = {2022-06-10T06:09:17.908Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2022-06-10T06:09:17.908Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Nguyen, Phong and Karnewar, Animesh and Huynh, Lam and Rahtu, Esa and Matas, Jiri and Heikkila, Janne},\n doi = {10.1109/3DV53792.2021.00117},\n booktitle = {2021 International Conference on 3D Vision (3DV)}\n}

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\n \n 2020\n \n \n (3)\n \n \n

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\n \n\n \n \n \n \n \n \n LSD2 - Joint Denoising and Deblurring of Short and Long Exposure Images with Convolutional Neural Networks.\n \n \n \n \n\n\n \n Mustaniemi, J.; Kannala, J.; Matas, J.; Särkkä, S.; and Heikkilä, J.\n\n\n \n\n\n\n In Proc. 31st British Machine Vision Conference, 2020. \n \n\n\n\n
\n\n\n\n \n \n $\"LSD2$ Website\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {LSD2 - Joint Denoising and Deblurring of Short and Long Exposure Images with Convolutional Neural Networks},\n type = {inproceedings},\n year = {2020},\n websites = {http://arxiv.org/abs/1811.09485},\n id = {c764775e-2439-324b-ac36-9d18d9cf0d23},\n created = {2019-09-15T16:34:29.422Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2021-04-25T09:07:55.874Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Mustaniemi2018a},\n source_type = {JOUR},\n folder_uuids = {67b5fbfc-903a-4f35-8d95-f58fc1430bfd},\n private_publication = {false},\n abstract = {The paper addresses the problem of acquiring highquality photographs with handheld smartphone cameras in low-light imaging conditions. We propose an approach based on capturing pairs of short and long exposure images in rapid succession and fusing them into a single highquality photograph. Unlike existing methods, we take advantage of both images simultaneously and perform a joint denoising and deblurring using a convolutional neural network. The network is trained using a combination of real and simulated data. To that end, we introduce a novel approach for generating realistic short-long exposure image pairs. The evaluation shows that the method produces good images in extremely challenging conditions and outperforms existing denoising and deblurring methods. Furthermore, it enables exposure fusion even in the presence of motion blur.},\n bibtype = {inproceedings},\n author = {Mustaniemi, Janne and Kannala, Juho and Matas, Jiri and Särkkä, Simo and Heikkilä, Janne},\n booktitle = {Proc. 31st British Machine Vision Conference}\n}

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\n \n\n \n \n \n \n \n \n A Sparse Resultant Based Method for Efficient Minimal Solvers.\n \n \n \n \n\n\n \n Bhayani, S.; Kukelova, Z.; and Heikkila, J.\n\n\n \n\n\n\n In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 1767-1776, 6 2020. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {A Sparse Resultant Based Method for Efficient Minimal Solvers},\n type = {inproceedings},\n year = {2020},\n pages = {1767-1776},\n websites = {https://ieeexplore.ieee.org/document/9156337/},\n month = {6},\n publisher = {IEEE},\n id = {0805d08d-369d-3246-84ea-798031235687},\n created = {2020-09-07T08:22:23.709Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2020-09-07T08:22:23.709Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Many computer vision applications require robust and efficient estimation of camera geometry. The robust estimation is usually based on solving camera geometry problems from a minimal number of input data measurements, i.e. solving minimal problems in a RANSAC framework. Minimal problems often result in complex systems of polynomial equations. Many state-of-the-art efficient polynomial solvers to these problems are based on Gr\\"obner bases and the action-matrix method that has been automatized and highly optimized in recent years. In this paper we study an alternative algebraic method for solving systems of polynomial equations, i.e., the sparse resultant-based method and propose a novel approach to convert the resultant constraint to an eigenvalue problem. This technique can significantly improve the efficiency and stability of existing resultant-based solvers. We applied our new resultant-based method to a large variety of computer vision problems and show that for most of the considered problems, the new method leads to solvers that are the same size as the the best available Gr\\"obner basis solvers and of similar accuracy. For some problems the new sparse-resultant based method leads to even smaller and more stable solvers than the state-of-the-art Gr\\"obner basis solvers. Our new method can be fully automatized and incorporated into existing tools for automatic generation of efficient polynomial solvers and as such it represents a competitive alternative to popular Gr\\"obner basis methods for minimal problems in computer vision.},\n bibtype = {inproceedings},\n author = {Bhayani, Snehal and Kukelova, Zuzana and Heikkila, Janne},\n doi = {10.1109/CVPR42600.2020.00184},\n booktitle = {2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}\n}

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\n \n\n \n \n \n \n \n \n Guiding Monocular Depth Estimation Using Depth-Attention Volume.\n \n \n \n \n\n\n \n Huynh, L.; Nguyen-Ha, P.; Matas, J.; Rahtu, E.; and Heikkilä, J.\n\n\n \n\n\n\n Volume 12371 LNCS . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pages 581-597. 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Lecture$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

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@inbook{\n type = {inbook},\n year = {2020},\n keywords = {Attention mechanism,Depth estimation,Monocular depth},\n pages = {581-597},\n volume = {12371 LNCS},\n websites = {https://link.springer.com/10.1007/978-3-030-58574-7_35},\n id = {31935092-94d7-3174-afcf-bfa4369c2dbc},\n created = {2022-06-10T06:09:17.807Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2022-06-10T06:09:17.807Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Recovering the scene depth from a single image is an ill-posed problem that requires additional priors, often referred to as monocular depth cues, to disambiguate different 3D interpretations. In recent works, those priors have been learned in an end-to-end manner from large datasets by using deep neural networks. In this paper, we propose guiding depth estimation to favor planar structures that are ubiquitous especially in indoor environments. This is achieved by incorporating a non-local coplanarity constraint to the network with a novel attention mechanism called depth-attention volume (DAV). Experiments on two popular indoor datasets, namely NYU-Depth-v2 and ScanNet, show that our method achieves state-of-the-art depth estimation results while using only a fraction of the number of parameters needed by the competing methods. Code is available at: https://github.com/HuynhLam/DAV.},\n bibtype = {inbook},\n author = {Huynh, Lam and Nguyen-Ha, Phong and Matas, Jiri and Rahtu, Esa and Heikkilä, Janne},\n doi = {10.1007/978-3-030-58574-7_35},\n chapter = {Guiding Monocular Depth Estimation Using Depth-Attention Volume},\n title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}\n}

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\n \n 2019\n \n \n (8)\n \n \n

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\n \n\n \n \n \n \n \n \n A Star Sensor On-Orbit Calibration Method Based on Singular Value Decomposition.\n \n \n \n \n\n\n \n Wu, L.; Xu, Q.; Heikkilä, J.; Zhao, Z.; Liu, L.; and Niu, a., Y.\n\n\n \n\n\n\n Sensors, 19(15): 3301. 7 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{\n title = {A Star Sensor On-Orbit Calibration Method Based on Singular Value Decomposition},\n type = {article},\n year = {2019},\n pages = {3301},\n volume = {19},\n websites = {https://www.mdpi.com/1424-8220/19/15/3301},\n month = {7},\n day = {26},\n id = {4f19f7f9-9cd9-37ca-9241-d41e6a1606be},\n created = {2019-09-15T16:34:29.252Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-11-20T19:31:02.510Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Wu2019},\n source_type = {JOUR},\n private_publication = {false},\n abstract = {The navigation accuracy of a star sensor depends on the estimation accuracy of its optical parameters, and so, the parameters should be updated in real time to obtain the best performance. Current on-orbit calibration methods for star sensors mainly rely on the angular distance between stars, and few studies have been devoted to seeking new calibration references. In this paper, an on-orbit calibration method using singular values as the calibration reference is introduced and studied. Firstly, the camera model of the star sensor is presented. Then, on the basis of the invariance of the singular values under coordinate transformation, an on-orbit calibration method based on the singular-value decomposition (SVD) method is proposed. By means of observability analysis, an optimal model of the star combinations for calibration is explored. According to the physical interpretation of the singular-value decomposition of the star vector matrix, the singular-value selection for calibration is discussed. Finally, to demonstrate the performance of the SVD method, simulation calibrations are conducted by both the SVD method and the conventional angular distance-based method. The results show that the accuracy and convergence speed of both methods are similar; however, the computational cost of the SVD method is heavily reduced. Furthermore, a field experiment is conducted to verify the feasibility of the SVD method. Therefore, the SVD method performs well in the calibration of star sensors, and in particular, it is suitable for star sensors with limited computing resources.},\n bibtype = {article},\n author = {Wu, Liang and Xu, Qian and Heikkilä, Janne and Zhao, Zijun and Liu, Liwei and Niu, and Yali},\n doi = {10.3390/s19153301},\n journal = {Sensors},\n number = {15}\n}

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\n \n\n \n \n \n \n \n \n Predicting Novel Views Using Generative Adversarial Query Network.\n \n \n \n \n\n\n \n Nguyen-Ha, P.; Huynh, L.; Rahtu, E.; and Heikkilä, J.\n\n\n \n\n\n\n In Image Analysis. SCIA 2019. Lecture Notes in Computer Science, volume 11482 LNCS, pages 16-27, 2019. Springer, Cham\n \n\n\n\n
\n\n\n\n \n \n $\"Predicting$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

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@inproceedings{\n title = {Predicting Novel Views Using Generative Adversarial Query Network},\n type = {inproceedings},\n year = {2019},\n keywords = {Generative Adversarial Query Network,Mean feature matching loss,Novel view synthesis},\n pages = {16-27},\n volume = {11482 LNCS},\n websites = {https://arxiv.org/abs/1904.05124},\n publisher = {Springer, Cham},\n id = {6efb068f-d347-35be-a9e4-fc2f29fd56a7},\n created = {2019-09-15T16:34:29.285Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-11-20T19:31:02.331Z},\n read = {true},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Nguyen-Ha2019},\n source_type = {CONF},\n private_publication = {false},\n abstract = {The problem of predicting a novel view of the scene using an arbitrary number of observations is a challenging problem for computers as well as for humans. This paper introduces the Generative Adversarial Query Network (GAQN), a general learning framework for novel view synthesis that combines Generative Query Network (GQN) and Generative Adversarial Networks (GANs). The conventional GQN encodes input views into a latent representation that is used to generate a new view through a recurrent variational decoder. The proposed GAQN builds on this work by adding two novel aspects: First, we extend the current GQN architecture with an adversarial loss function for improving the visual quality and convergence speed. Second, we introduce a feature-matching loss function for stabilizing the training procedure. The experiments demonstrate that GAQN is able to produce high-quality results and faster convergence compared to the conventional approach.},\n bibtype = {inproceedings},\n author = {Nguyen-Ha, Phong and Huynh, Lam and Rahtu, Esa and Heikkilä, Janne},\n doi = {10.1007/978-3-030-20205-7_2},\n booktitle = {Image Analysis. SCIA 2019. Lecture Notes in Computer Science}\n}

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\n \n\n \n \n \n \n \n \n Rethinking the Evaluation of Video Summaries.\n \n \n \n \n\n\n \n Otani, M.; Nakashima, Y.; Rahtu, E.; and Heikkilä, J.\n\n\n \n\n\n\n Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,7596-7604. 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Rethinking$ Website\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{\n title = {Rethinking the Evaluation of Video Summaries},\n type = {article},\n year = {2019},\n pages = {7596-7604},\n websites = {http://arxiv.org/abs/1903.11328},\n id = {e9cfddd9-b94e-3fd6-8a5f-3662e38b78eb},\n created = {2019-09-15T16:34:29.290Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-11-20T19:31:01.951Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Otani2019},\n source_type = {CONF},\n private_publication = {false},\n abstract = {Video summarization is a technique to create a short skim of the original video while preserving the main stories/content. There exists a substantial interest in automatizing this process due to the rapid growth of the available material. The recent progress has been facilitated by public benchmark datasets, which enable easy and fair comparison of methods. Currently the established evaluation protocol is to compare the generated summary with respect to a set of reference summaries provided by the dataset. In this paper, we will provide in-depth assessment of this pipeline using two popular benchmark datasets. Surprisingly, we observe that randomly generated summaries achieve comparable or better performance to the state-of-the-art. In some cases, the random summaries outperform even the human generated summaries in leave-one-out experiments. Moreover, it turns out that the video segmentation, which is often considered as a fixed pre-processing method, has the most significant impact on the performance measure. Based on our observations, we propose alternative approaches for assessing the importance scores as well as an intuitive visualization of correlation between the estimated scoring and human annotations.},\n bibtype = {article},\n author = {Otani, Mayu and Nakashima, Yuta and Rahtu, Esa and Heikkilä, Janne},\n journal = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}\n}

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\n \n\n \n \n \n \n \n \n 3D Multi-Resolution Optical Flow Analysis of Cardiovascular Pulse Propagation in Human Brain.\n \n \n \n \n\n\n \n Rajna, Z.; Raitamaa, L.; Tuovinen, T.; Heikkilä, J.; Kiviniemi, V.; and Seppanen, T.\n\n\n \n\n\n\n IEEE Transactions on Medical Imaging, 38(9): 2028-2036. 9 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"3D$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{\n title = {3D Multi-Resolution Optical Flow Analysis of Cardiovascular Pulse Propagation in Human Brain},\n type = {article},\n year = {2019},\n pages = {2028-2036},\n volume = {38},\n websites = {https://ieeexplore.ieee.org/document/8667851/},\n month = {9},\n id = {e2ba7db1-8fcd-357d-8d4f-2d1cd1582ded},\n created = {2019-09-15T16:34:29.327Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-19T17:47:33.021Z},\n read = {true},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {JOUR},\n private_publication = {false},\n abstract = {The brain is cleaned from waste by glymphatic clearance serving a similar purpose as the lymphatic system in the rest of the body. Impairment of the glymphatic brain clearance precedes protein accumulation and reduced cognitive function in Alzheimer's disease (AD). Cardiovascular pulsations are a primary driving force of the glymphatic brain clearance. We developed a method to quantify cardiovascular pulse propagation in human brain with magnetic resonance encephalography (MREG). We extended a standard optical flow estimation method to three spatial dimensions, with a multi-resolution processing scheme. We added application specific criteria for discarding inaccurate results. With the proposed method, it is now possible to estimate the propagation of cardiovascular pulse wavefronts from the whole brain MREG data sampled at 10 Hz. The results show, that on average the cardiovascular pulse propagates from major arteries via cerebral spinal fluid spaces into all tissue compartments in the brain. We present an example, that cardiovascular pulsations are significantly altered in AD: coefficient of variation and sample entropy of the pulse propagation speed in the lateral ventricles change in AD. These changes are in line with the theory of glymphatic clearance impairment in AD. The proposed non-invasive method can assess a performance indicator related to the glymphatic clearance in the human brain.},\n bibtype = {article},\n author = {Rajna, Zalan and Raitamaa, Lauri and Tuovinen, Timo and Heikkilä, Janne and Kiviniemi, Vesa and Seppanen, Tapio},\n doi = {10.1109/TMI.2019.2904762},\n journal = {IEEE Transactions on Medical Imaging},\n number = {9}\n}

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\n \n\n \n \n \n \n \n \n An Efficient Solution for Semantic Segmentation: ShuffleNet V2 with Atrous Separable Convolutions.\n \n \n \n \n\n\n \n Türkmen, S.; and Heikkilä, J.\n\n\n \n\n\n\n In Image Analysis. SCIA 2019. Lecture Notes in Computer Science, volume 11482 LNCS, pages 41-53, 2019. Springer, Cham\n \n\n\n\n
\n\n\n\n \n \n $\"An$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@inproceedings{\n title = {An Efficient Solution for Semantic Segmentation: ShuffleNet V2 with Atrous Separable Convolutions},\n type = {inproceedings},\n year = {2019},\n keywords = {Efficient,Fast,Lightweight,Mobile,Real-time,Semantic image segmentation},\n pages = {41-53},\n volume = {11482 LNCS},\n websites = {https://arxiv.org/abs/1902.07476},\n publisher = {Springer, Cham},\n id = {f995f72e-f019-3c72-a506-7b19d6639252},\n created = {2019-09-15T16:34:29.380Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2020-09-23T11:35:23.556Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Turkmen2019},\n source_type = {CONF},\n folder_uuids = {67b5fbfc-903a-4f35-8d95-f58fc1430bfd},\n private_publication = {false},\n abstract = {Assigning a label to each pixel in an image, namely semantic segmentation, has been an important task in computer vision, and has applications in autonomous driving, robotic navigation, localization, and scene understanding. Fully convolutional neural networks have proved to be a successful solution for the task over the years but most of the work being done focuses primarily on accuracy. In this paper, we present a computationally efficient approach to semantic segmentation, while achieving a high mean intersection over union (mIOU), on Cityscapes challenge. The network proposed is capable of running real-time on mobile devices. In addition, we make our code and model weights publicly available.},\n bibtype = {inproceedings},\n author = {Türkmen, Sercan and Heikkilä, Janne},\n doi = {10.1007/978-3-030-20205-7_4},\n booktitle = {Image Analysis. SCIA 2019. Lecture Notes in Computer Science}\n}

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\n \n\n \n \n \n \n \n \n Dynamic Texture Classification Using Unsupervised 3D Filter Learning and Local Binary Encoding.\n \n \n \n \n\n\n \n Zhao, X.; Lin, Y.; Liu, L.; Heikkila, J.; and Zheng, W.\n\n\n \n\n\n\n IEEE Transactions on Multimedia, 21(7): 1694-1708. 7 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Dynamic$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Dynamic Texture Classification Using Unsupervised 3D Filter Learning and Local Binary Encoding},\n type = {article},\n year = {2019},\n keywords = {Dynamic texture,feature extraction,local binary pattern,motion},\n pages = {1694-1708},\n volume = {21},\n websites = {https://ieeexplore.ieee.org/document/8600380/},\n month = {7},\n id = {5c32da76-aec8-30a2-8a4b-99e58b18b2bc},\n created = {2019-09-15T16:34:29.418Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-11-20T19:31:02.136Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Zhao2019},\n source_type = {JOUR},\n private_publication = {false},\n abstract = {Local binary descriptors, such as local binary pattern (LBP) and its various variants, have been studied extensively in texture and dynamic texture analysis due to their outstanding characteristics, such as grayscale invariance, low computational complexity and good discriminability. Most existing local binary feature extraction methods extract spatio-temporal features from three orthogonal planes of a spatio-temporal volume by viewing a dynamic texture in 3D space. For a given pixel in a video, only a proportion of its surrounding pixels is incorporated in the local binary feature extraction process. We argue that the ignored pixels contain discriminative information that should be explored. To fully utilize the information conveyed by all the pixels in a local neighborhood, we propose extracting local binary features from the spatio-temporal domain with 3D filters that are learned in an unsupervised manner so that the discriminative features along both the spatial and temporal dimensions are captured simultaneously. The proposed approach consists of three components: 1) 3D filtering; 2) binary hashing; and 3) joint histogramming. Densely sampled 3D blocks of a dynamic texture are first normalized to have zero mean and are then filtered by 3D filters that are learned in advance. To preserve more of the structure information, the filter response vectors are decomposed into two complementary components, namely, the signs and the magnitudes, which are further encoded separately into binary codes. The local mean pixels of the 3D blocks are also converted into binary codes. Finally, three types of binary codes are combined via joint or hybrid histograms for the final feature representation. Extensive experiments are conducted on three commonly used dynamic texture databases: 1) UCLA; 2) DynTex; and 3) YUVL. The proposed method provides comparable results to, and even outperforms, many state-of-the-art methods.},\n bibtype = {article},\n author = {Zhao, Xiaochao and Lin, Yaping and Liu, Li and Heikkila, Janne and Zheng, Wenming},\n doi = {10.1109/TMM.2018.2890362},\n journal = {IEEE Transactions on Multimedia},\n number = {7}\n}

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\n \n\n \n \n \n \n \n \n Gyroscope-Aided Motion Deblurring with Deep Networks.\n \n \n \n \n\n\n \n Mustaniemi, J.; Kannala, J.; Sarkka, S.; Matas, J.; and Heikkila, J.\n\n\n \n\n\n\n In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pages 1914-1922, 1 2019. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"Gyroscope-Aided$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Gyroscope-Aided Motion Deblurring with Deep Networks},\n type = {inproceedings},\n year = {2019},\n pages = {1914-1922},\n websites = {https://arxiv.org/abs/1810.00986},\n month = {1},\n publisher = {IEEE},\n id = {80104f3c-95b1-3227-9495-4da11a651559},\n created = {2019-09-19T17:36:36.954Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2020-09-23T11:35:23.411Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Mustaniemi2019},\n source_type = {CONF},\n folder_uuids = {67b5fbfc-903a-4f35-8d95-f58fc1430bfd},\n private_publication = {false},\n abstract = {We propose a deblurring method that incorporates gyroscope measurements into a convolutional neural network (CNN). With the help of such measurements, it can handle extremely strong and spatially-variant motion blur. At the same time, the image data is used to overcome the limitations of gyro-based blur estimation. To train our network, we also introduce a novel way of generating realistic training data using the gyroscope. The evaluation shows a clear improvement in visual quality over the state-of-the-art while achieving real-time performance. Furthermore, the method is shown to improve the performance of existing feature detectors and descriptors against the motion blur.},\n bibtype = {inproceedings},\n author = {Mustaniemi, Janne and Kannala, Juho and Sarkka, Simo and Matas, Jiri and Heikkila, Janne},\n doi = {10.1109/WACV.2019.00208},\n booktitle = {2019 IEEE Winter Conference on Applications of Computer Vision (WACV)}\n}

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\n \n\n \n \n \n \n \n An efficient solution for semantic segmentation: ShuffleNet V2 with atrous separable convolutions.\n \n \n \n\n\n \n Türkmen, S.; and Heikkilä, J.\n\n\n \n\n\n\n 2019.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@misc{\n title = {An efficient solution for semantic segmentation: ShuffleNet V2 with atrous separable convolutions},\n type = {misc},\n year = {2019},\n source = {arXiv},\n keywords = {Efficient,Fast · lightweight,Mobile,Real-time,Semantic image segmentation},\n id = {ebf9aead-a5f8-3359-a557-db1d8a77eab2},\n created = {2020-10-28T23:59:00.000Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2020-11-01T08:33:02.317Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n private_publication = {true},\n abstract = {Copyright © 2019, arXiv, All rights reserved. Assigning a label to each pixel in an image, namely semantic segmentation, has been an important task in computer vision, and has applications in autonomous driving, robotic navigation, localization, and scene understanding. Fully convolutional neural networks have proved to be a successful solution for the task over the years but most of the work being done focuses primarily on accuracy. In this paper, we present a computationally efficient approach to semantic segmentation, while achieving a high mean intersection over union (mIOU), 70.33% on Cityscapes challenge. The network proposed is capable of running real-time on mobile devices. In addition, we make our code and model weights publicly available.},\n bibtype = {misc},\n author = {Türkmen, S. and Heikkilä, J.}\n}

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\n \n 2018\n \n \n (5)\n \n \n

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\n \n\n \n \n \n \n \n \n Leveraging Unlabeled Whole-Slide-Images for Mitosis Detection.\n \n \n \n \n\n\n \n Akram, S., U.; Qaiser, T.; Graham, S.; Kannala, J.; Heikkilä, J.; and Rajpoot, N.\n\n\n \n\n\n\n Volume 11039 LNCS . Computational Pathology and Ophthalmic Medical Image Analysis. OMIA 2018, COMPAY 2018. Lecture Notes in Computer Science, pages 69-77. Springer, Cham, 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Computational$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@inbook{\n type = {inbook},\n year = {2018},\n keywords = {Breast cancer,Computational pathology,Mitosis detection,Self-supervised learning,Semi-supervised learning},\n pages = {69-77},\n volume = {11039 LNCS},\n websites = {http://link.springer.com/10.1007/978-3-030-00949-6_9},\n publisher = {Springer, Cham},\n id = {ad005696-bb09-3704-abc8-84b90465335e},\n created = {2019-09-15T16:34:29.481Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-11-20T19:31:01.957Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Akram2018},\n source_type = {CHAP},\n private_publication = {false},\n abstract = {Mitosis count is an important biomarker for prognosis of various cancers. At present, pathologists typically perform manual counting on a few selected regions of interest in breast whole-slide-images (WSIs) of patient biopsies. This task is very time-consuming, tedious and subjective. Automated mitosis detection methods have made great advances in recent years. However, these methods require exhaustive labeling of a large number of selected regions of interest. This task is very expensive because expert pathologists are needed for reliable and accurate annotations. In this paper, we present a semi-supervised mitosis detection method which is designed to leverage a large number of unlabeled breast cancer WSIs. As a result, our method capitalizes on the growing number of digitized histology images, without relying on exhaustive annotations, subsequently improving mitosis detection. Our method first learns a mitosis detector from labeled data, uses this detector to mine additional mitosis samples from unlabeled WSIs, and then trains the final model using this larger and diverse set of mitosis samples. The use of unlabeled data improves F1-score by $\\sim$5\\% compared to our best performing fully-supervised model on the TUPAC validation set. Our submission (single model) to TUPAC challenge ranks highly on the leaderboard with an F1-score of 0.64.},\n bibtype = {inbook},\n author = {Akram, Saad Ullah and Qaiser, Talha and Graham, Simon and Kannala, Juho and Heikkilä, Janne and Rajpoot, Nasir},\n doi = {10.1007/978-3-030-00949-6_9},\n chapter = {Leveraging Unlabeled Whole-Slide-Images for Mitosis Detection},\n title = {Computational Pathology and Ophthalmic Medical Image Analysis. OMIA 2018, COMPAY 2018. Lecture Notes in Computer Science}\n}

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\n \n\n \n \n \n \n \n \n Editorial Note: Low Resource Media Computing in the Big Data Era.\n \n \n \n \n\n\n \n Xie, L.; Heikkilä, J.; and Li, B.\n\n\n \n\n\n\n Multimedia Tools and Applications, 77(14): 18761-18761. 7 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Editorial$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{\n title = {Editorial Note: Low Resource Media Computing in the Big Data Era},\n type = {article},\n year = {2018},\n pages = {18761-18761},\n volume = {77},\n websites = {http://link.springer.com/10.1007/s11042-018-6140-0},\n month = {7},\n day = {13},\n id = {69dcac73-2d90-3a6f-aa37-b857294aaa1d},\n created = {2019-09-15T16:34:29.521Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-11-20T19:31:02.325Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Xie2018},\n source_type = {JOUR},\n private_publication = {false},\n bibtype = {article},\n author = {Xie, Lei and Heikkilä, Janne and Li, Bo},\n doi = {10.1007/s11042-018-6140-0},\n journal = {Multimedia Tools and Applications},\n number = {14}\n}

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\n \n\n \n \n \n \n \n \n Accurate 3-D Reconstruction with RGB-D Cameras using Depth Map Fusion and Pose Refinement.\n \n \n \n \n\n\n \n Ylimaki, M.; Heikkila, J.; and Kannala, J.\n\n\n \n\n\n\n In 2018 24th International Conference on Pattern Recognition (ICPR), volume 2018-Augus, pages 1977-1982, 8 2018. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"Accurate$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Accurate 3-D Reconstruction with RGB-D Cameras using Depth Map Fusion and Pose Refinement},\n type = {inproceedings},\n year = {2018},\n pages = {1977-1982},\n volume = {2018-Augus},\n websites = {https://ieeexplore.ieee.org/document/8545508/},\n month = {8},\n publisher = {IEEE},\n id = {813fee06-844b-3774-a23b-2c06f483d693},\n created = {2019-09-15T16:34:29.556Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-11-20T19:31:02.526Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Ylimaki2018},\n source_type = {CONF},\n private_publication = {false},\n abstract = {Depth map fusion is an essential part in both stereo and RGB-D based 3-D reconstruction pipelines. Whether produced with a passive stereo reconstruction or using an active depth sensor, such as Microsoft Kinect, the depth maps have noise and may have poor initial registration. In this paper, we introduce a method which is capable of handling outliers, and especially, even significant registration errors. The proposed method first fuses a sequence of depth maps into a single non-redundant point cloud so that the redundant points are merged together by giving more weight to more certain measurements. Then, the original depth maps are re-registered to the fused point cloud to refine the original camera extrinsic parameters. The fusion is then performed again with the refined extrinsic parameters. This procedure is repeated until the result is satisfying or no significant changes happen between iterations. The method is robust to outliers and erroneous depth measurements as well as even significant depth map registration errors due to inaccurate initial camera poses.},\n bibtype = {inproceedings},\n author = {Ylimaki, Markus and Heikkila, Janne and Kannala, Juho},\n doi = {10.1109/ICPR.2018.8545508},\n booktitle = {2018 24th International Conference on Pattern Recognition (ICPR)}\n}

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\n \n\n \n \n \n \n \n \n Fast Motion Deblurring for Feature Detection and Matching Using Inertial Measurements.\n \n \n \n \n\n\n \n Mustaniemi, J.; Kannala, J.; Sarkka, S.; Matas, J.; and Heikkila, J.\n\n\n \n\n\n\n In 2018 24th International Conference on Pattern Recognition (ICPR), pages 3068-3073, 8 2018. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"Fast$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Fast Motion Deblurring for Feature Detection and Matching Using Inertial Measurements},\n type = {inproceedings},\n year = {2018},\n pages = {3068-3073},\n websites = {https://arxiv.org/abs/1805.08542},\n month = {8},\n publisher = {IEEE},\n id = {468c5d7d-14e7-3a82-9abd-60e7e567f9bf},\n created = {2019-09-15T16:34:29.560Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2020-09-23T11:35:23.261Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Mustaniemi2018},\n source_type = {CONF},\n folder_uuids = {67b5fbfc-903a-4f35-8d95-f58fc1430bfd},\n private_publication = {false},\n abstract = {Many computer vision and image processing applications rely on local features. It is well-known that motion blur decreases the performance of traditional feature detectors and descriptors. We propose an inertial-based deblurring method for improving the robustness of existing feature detectors and descriptors against the motion blur. Unlike most deblurring algorithms, the method can handle spatially-variant blur and rolling shutter distortion. Furthermore, it is capable of running in real-time contrary to state-of-the-art algorithms. The limitations of inertial-based blur estimation are taken into account by validating the blur estimates using image data. The evaluation shows that when the method is used with traditional feature detector and descriptor, it increases the number of detected keypoints, provides higher repeatability and improves the localization accuracy. We also demonstrate that such features will lead to more accurate and complete reconstructions when used in the application of 3D visual reconstruction.},\n bibtype = {inproceedings},\n author = {Mustaniemi, Janne and Kannala, Juho and Sarkka, Simo and Matas, Jiri and Heikkila, Janne},\n doi = {10.1109/ICPR.2018.8546041},\n booktitle = {2018 24th International Conference on Pattern Recognition (ICPR)}\n}

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\n \n\n \n \n \n \n \n \n Dynamic Texture Recognition Using Volume Local Binary Count Patterns With an Application to 2D Face Spoofing Detection.\n \n \n \n \n\n\n \n Zhao, X.; Lin, Y.; and Heikkila, J.\n\n\n \n\n\n\n IEEE Transactions on Multimedia, 20(3): 552-566. 3 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Dynamic$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Dynamic Texture Recognition Using Volume Local Binary Count Patterns With an Application to 2D Face Spoofing Detection},\n type = {article},\n year = {2018},\n keywords = {2D face spoofing detection,Dynamic texture,spatio-Temporal descriptor,volume local binary count},\n pages = {552-566},\n volume = {20},\n websites = {http://ieeexplore.ieee.org/document/8030131/},\n month = {3},\n id = {9bc3d800-2a26-3798-810f-42144d0a9f9d},\n created = {2019-09-15T16:34:29.739Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-11-20T19:31:02.137Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Zhao2018},\n source_type = {JOUR},\n private_publication = {false},\n abstract = {In this paper, a local spatiotemporal descriptor, namely, the volume local binary count (VLBC), is proposed for the representation and recognition of dynamic texture. This descriptor, which is similar in spirit to the volume local binary pattern (VLBP), extracts histograms of thresholded local spatiotemporal volumes using both appearance and motion features to describe dynamic texture. Unlike VLBP using binary encoding, VLBC does not exploit the local structure information and only counts the number of 1s in the thresholded codes. Thus, VLBC can include more neighboring pixels without exponentially increasing the feature dimension as VLBP does. Furthermore, a completed version of VLBC (CVLBC) is also proposed to enhance the performance of dynamic texture recognition with additional information about local contrast and central pixel intensities. The proposed method is not only efficient to compute but also effective for dynamic texture representation. In experiments with three dynamic texture databases, namely, UCLA, DynTex, and DynTex++, the proposed method produces classification rates that are comparable to those produced by the state-of-the-art approaches. In addition to dynamic texture recognition, we propose utilizing CVLBC for 2-D face spoofing detection. As an effective spatiotemporal descriptor, CVLBC can well describe the differences between facial videos of valid users and impostors, thus achieving good performance for face spoofing detection. For comparison with other methods, the proposed method is evaluated on three face antispoofing databases: Print-Attack, Replay-Attack, and CAS Face Antispoofing. The experimental results demonstrate the effectiveness of CVLBC for 2-D face spoofing detection.},\n bibtype = {article},\n author = {Zhao, Xiaochao and Lin, Yaping and Heikkila, Janne},\n doi = {10.1109/TMM.2017.2750415},\n journal = {IEEE Transactions on Multimedia},\n number = {3}\n}

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\n \n 2017\n \n \n (10)\n \n \n

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\n \n\n \n \n \n \n \n \n Dynamic texture recognition using multiscale PCA-learned filters.\n \n \n \n \n\n\n \n Zhao, X.; Lin, Y.; and Heikkila, J.\n\n\n \n\n\n\n In 2017 IEEE International Conference on Image Processing (ICIP), pages 4152-4156, 9 2017. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"Dynamic$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

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@inproceedings{\n title = {Dynamic texture recognition using multiscale PCA-learned filters},\n type = {inproceedings},\n year = {2017},\n keywords = {Dynamic texture recognition,Multiscale analysis,PCA-based filter learning},\n pages = {4152-4156},\n websites = {http://ieeexplore.ieee.org/document/8297064/},\n month = {9},\n publisher = {IEEE},\n id = {84dd6bbd-f5fc-3413-b501-342c3ee3d70f},\n created = {2019-09-15T16:34:29.601Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-11-20T19:31:02.124Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Zhao2017},\n source_type = {CONF},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Zhao, Xiaochao and Lin, Yaping and Heikkila, Janne},\n doi = {10.1109/ICIP.2017.8297064},\n booktitle = {2017 IEEE International Conference on Image Processing (ICIP)}\n}

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\n \n\n \n \n \n \n \n \n HNF1B controls epithelial organization and cell polarity during ureteric bud branching and collecting duct morphogenesis.\n \n \n \n \n\n\n \n Desgrange, A.; Heliot, C.; Skovorodkin, I.; Akram, S., U.; Heikkilä, J.; Ronkainen, V.; Miinalainen, I.; Vainio, S., J.; and Cereghini, S.\n\n\n \n\n\n\n Development, 144(24): 4704-4719. 12 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"HNF1B$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {HNF1B controls epithelial organization and cell polarity during ureteric bud branching and collecting duct morphogenesis},\n type = {article},\n year = {2017},\n keywords = {Branching morphogenesis,Cell polarity,Collecting duct,Gdnf-Gfrα1-Ret pathway,Kidney,Transcriptional regulation},\n pages = {4704-4719},\n volume = {144},\n websites = {http://dev.biologists.org/lookup/doi/10.1242/dev.154336},\n month = {12},\n day = {15},\n id = {b8ebb570-9efc-3629-965f-f71f94ae57f8},\n created = {2019-09-15T16:34:29.684Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-11-20T19:31:01.970Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Desgrange2017},\n source_type = {JOUR},\n private_publication = {false},\n abstract = {Kidney development depends crucially on proper ureteric bud branching giving rise to the entire collecting duct system. The transcription factor HNF1B is required for the early steps of ureteric bud branching, yet the molecular and cellular events regulated by HNF1B are poorly understood. We report that specific removal of Hnf1b from the ureteric bud leads to defective cell-cell contacts and apicobasal polarity during the early branching events. High-resolution ex vivo imaging combined with a membranous fluorescent reporter strategy show decreased mutant cell rearrangements during mitosis-associated cell dispersal and severe epithelial disorganization. Molecular analysis reveals downregulation of Gdnf-Ret pathway components and suggests that HNF1B acts both upstream and downstream of Ret signaling by directly regulating Gfra1 and Etv5 Subsequently, Hnf1b deletion leads to massively mispatterned ureteric tree network, defective collecting duct differentiation and disrupted tissue architecture, which leads to cystogenesis. Consistently, mRNA-seq analysis shows that the most impacted genes encode intrinsic cell-membrane components with transporter activity. Our study uncovers a fundamental and recurring role of HNF1B in epithelial organization during early ureteric bud branching and in further patterning and differentiation of the collecting duct system in mouse.},\n bibtype = {article},\n author = {Desgrange, Audrey and Heliot, Claire and Skovorodkin, Ilya and Akram, Saad U and Heikkilä, Janne and Ronkainen, Veli-Pekka and Miinalainen, Ilkka and Vainio, Seppo J and Cereghini, Silvia},\n doi = {10.1242/dev.154336},\n journal = {Development},\n number = {24}\n}

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\n \n\n \n \n \n \n \n \n Using Sparse Elimination for Solving Minimal Problems in Computer Vision.\n \n \n \n \n\n\n \n Heikkila, J.\n\n\n \n\n\n\n In 2017 IEEE International Conference on Computer Vision (ICCV), pages 76-84, 10 2017. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"Using$ Paper\n \n \n \n $\"Using$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Using Sparse Elimination for Solving Minimal Problems in Computer Vision},\n type = {inproceedings},\n year = {2017},\n pages = {76-84},\n websites = {http://openaccess.thecvf.com/content_ICCV_2017/papers/Heikkila_Using_Sparse_Elimination_ICCV_2017_paper},\n month = {10},\n publisher = {IEEE},\n id = {da3fbf15-47de-3aae-91b0-0b673a840f46},\n created = {2019-09-15T16:34:29.704Z},\n file_attached = {true},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2020-06-03T17:39:45.778Z},\n read = {true},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Heikkila2017},\n source_type = {CONF},\n folder_uuids = {0f0cf5b0-9e2b-4b9f-889c-1f2db2122637},\n private_publication = {false},\n abstract = {Finding a closed form solution to a system of polynomial equations is a common problem in computer vision as well as in many other areas of engineering and science. Groebner basis techniques are often employed to provide the solution, but implementing an efficient Groebner basis solver to a given problem requires strong expertise in algebraic geometry. One can also convert the equations to a polynomial eigenvalue problem (PEP) and solve it using linear algebra, which is a more accessible approach for those who are not so familiar with algebraic geometry. In previous works PEP has been successfully applied for solving some relative pose problems in computer vision, but its wider exploitation is limited by the problem of finding a compact monomial basis. In this paper, we propose a new algorithm for selecting the basis that is in general more compact than the basis obtained with a state-of-the-art algorithm making PEP a more viable option for solving polynomial equations. Another contribution is that we present two minimal problems for camera self-calibration based on homography, and demonstrate experimentally using synthetic and real data that our algorithm can provide a numerically stable solution to the camera focal length from two homographies of unknown planar scene.},\n bibtype = {inproceedings},\n author = {Heikkila, Janne},\n doi = {10.1109/ICCV.2017.18},\n booktitle = {2017 IEEE International Conference on Computer Vision (ICCV)}\n}

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\n \n\n \n \n \n \n \n \n Towards Virtual H&E Staining of Hyperspectral Lung Histology Images Using Conditional Generative Adversarial Networks.\n \n \n \n \n\n\n \n Bayramoglu, N.; Kaakinen, M.; Eklund, L.; and Heikkila, J.\n\n\n \n\n\n\n In 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pages 64-71, 10 2017. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"Towards$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Towards Virtual H&amp;E Staining of Hyperspectral Lung Histology Images Using Conditional Generative Adversarial Networks},\n type = {inproceedings},\n year = {2017},\n pages = {64-71},\n websites = {http://ieeexplore.ieee.org/document/8265226/},\n month = {10},\n publisher = {IEEE},\n id = {d6411bf0-d9d8-300f-ba3c-14d8f6952f69},\n created = {2019-09-15T16:34:29.733Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-11-20T19:31:01.933Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Bayramoglu2017},\n source_type = {CONF},\n private_publication = {false},\n abstract = {In an era of online retrieval, it is appropriate to offer guidance to users wishing to improve their initial queries. One form of such guidance could be short lists of suggested terms gathered from feedback, nearest neighbors, and term variants of original query terms. To verify this approach, a series of experiments were run using the Cranfield test collection to discover techniques to select terms for these lists that would be effective for further retrieval. The results show that significant improvement can be expected from this approach to query expansion.},\n bibtype = {inproceedings},\n author = {Bayramoglu, Neslihan and Kaakinen, Mika and Eklund, Lauri and Heikkila, Janne},\n doi = {10.1109/ICCVW.2017.15},\n booktitle = {2017 IEEE International Conference on Computer Vision Workshops (ICCVW)}\n}

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\n \n\n \n \n \n \n \n \n Media computing and applications for immersive communications: recent advances.\n \n \n \n \n\n\n \n Xie, L.; Heikkilä, J.; and Li, B.\n\n\n \n\n\n\n Journal of Ambient Intelligence and Humanized Computing, 8(6): 827-828. 11 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Media$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{\n title = {Media computing and applications for immersive communications: recent advances},\n type = {article},\n year = {2017},\n pages = {827-828},\n volume = {8},\n websites = {http://link.springer.com/10.1007/s12652-017-0559-4},\n month = {11},\n day = {28},\n id = {1b4ec01c-251b-39ac-b386-7a38aee6870d},\n created = {2019-09-15T16:34:29.772Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-11-20T19:31:02.318Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Xie2017},\n source_type = {JOUR},\n private_publication = {false},\n bibtype = {article},\n author = {Xie, Lei and Heikkilä, Janne and Li, Bo},\n doi = {10.1007/s12652-017-0559-4},\n journal = {Journal of Ambient Intelligence and Humanized Computing},\n number = {6}\n}

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\n \n\n \n \n \n \n \n \n Robust and Practical Depth Map Fusion for Time-of-Flight Cameras.\n \n \n \n \n\n\n \n Ylimäki, M.; Kannala, J.; and Heikkilä, J.\n\n\n \n\n\n\n Volume 10269 LNCS . Image Analysis. SCIA 2017. Lecture Notes in Computer Science, pages 122-134. Springer, Cham, 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Image$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n\n\n\n

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@inbook{\n type = {inbook},\n year = {2017},\n keywords = {Depth map merging,RGB-D reconstruction},\n pages = {122-134},\n volume = {10269 LNCS},\n websites = {http://link.springer.com/10.1007/978-3-319-59126-1_11},\n publisher = {Springer, Cham},\n id = {5e66c3bf-b172-3acc-8e8b-ec901d0cd260},\n created = {2019-09-15T16:34:29.776Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-11-20T19:31:01.970Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Ylimaki2017},\n source_type = {CONF},\n private_publication = {false},\n abstract = {Fusion of overlapping depth maps is an important part in many 3D reconstruction pipelines. Ideally fusion produces an accurate and nonredundant point cloud robustly even from noisy and partially poorly registered depth maps. In this paper, we improve an existing fusion algorithm towards a more ideal solution. Our method builds a nonredundant point cloud from a sequence of depth maps so that the new measurements are either added to the existing point cloud if they are in an area which is not yet covered or used to refine the existing points. The method is robust to outliers and erroneous depth measurements as well as small depth map registration errors due to inaccurate camera poses. The results show that the method overcomes its predecessor both in accuracy and robustness.},\n bibtype = {inbook},\n author = {Ylimäki, Markus and Kannala, Juho and Heikkilä, Janne},\n doi = {10.1007/978-3-319-59126-1_11},\n chapter = {Robust and Practical Depth Map Fusion for Time-of-Flight Cameras},\n title = {Image Analysis. SCIA 2017. Lecture Notes in Computer Science}\n}

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\n \n\n \n \n \n \n \n \n Novel fixed z -direction (FiZD) kidney primordia and an organoid culture system for time-lapse confocal imaging.\n \n \n \n \n\n\n \n Saarela, U.; Akram, S., U.; Desgrange, A.; Rak-Raszewska, A.; Shan, J.; Cereghini, S.; Ronkainen, V.; Heikkilä, J.; Skovorodkin, I.; and Vainio, S., J.\n\n\n \n\n\n\n Development, 144(6): 1113-1117. 3 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Novel$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Novel fixed z -direction (FiZD) kidney primordia and an organoid culture system for time-lapse confocal imaging},\n type = {article},\n year = {2017},\n keywords = {Imaging,Kidney,Organ culture,Organoid,Time-lapse},\n pages = {1113-1117},\n volume = {144},\n websites = {http://dev.biologists.org/lookup/doi/10.1242/dev.142950},\n month = {3},\n day = {15},\n id = {f2b8030f-bfc9-38dc-bdde-12363514b654},\n created = {2019-09-15T16:34:29.816Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-11-20T19:31:02.309Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Saarela2017},\n source_type = {JOUR},\n private_publication = {false},\n abstract = {Tissue, organ and organoid cultures provide suitable models for developmental studies, but our understanding of how the organs are assembled at the single-cell level still remains unclear. We describe here a novel fixedz-direction (FiZD) culture setup that permits high-resolution confocal imaging of organoids and embryonic tissues. In a FiZD culture a permeable membrane compresses the tissues onto a glass coverslip and the spacers adjust the thickness, enabling the tissue to grow for up to 12 days. Thus, the kidney rudiment and the organoids can adjust to the limitedz-directional space and yet advance the process of kidney morphogenesis, enabling long-term time-lapse and high-resolution confocal imaging. As the data quality achieved was sufficient for computer-assisted cell segmentation and analysis, the method can be used for studying morphogenesisex vivoat the level of the single constituent cells of a complex mammalian organogenesis model system.},\n bibtype = {article},\n author = {Saarela, Ulla and Akram, Saad Ullah and Desgrange, Audrey and Rak-Raszewska, Aleksandra and Shan, Jingdong and Cereghini, Silvia and Ronkainen, Veli-Pekka and Heikkilä, Janne and Skovorodkin, Ilya and Vainio, Seppo J},\n doi = {10.1242/dev.142950},\n journal = {Development},\n number = {6}\n}

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\n \n\n \n \n \n \n \n \n Cell Tracking via Proposal Generation and Selection.\n \n \n \n \n\n\n \n Akram, S., U.; Kannala, J.; Eklund, L.; and Heikkilä, J.\n\n\n \n\n\n\n arXiv preprint arXiv:1705.03386. 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Cell$ Website\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{\n title = {Cell Tracking via Proposal Generation and Selection},\n type = {article},\n year = {2017},\n websites = {http://arxiv.org/abs/1705.03386},\n id = {ee5471c3-2bd5-3650-91ad-b8a8c6e328b8},\n created = {2019-09-15T16:34:29.820Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-11-20T19:31:02.477Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Akram2017},\n source_type = {JOUR},\n private_publication = {false},\n abstract = {Microscopy imaging plays a vital role in understanding many biological processes in development and disease. The recent advances in automation of microscopes and development of methods and markers for live cell imaging has led to rapid growth in the amount of image data being captured. To efficiently and reliably extract useful insights from these captured sequences, automated cell tracking is essential. This is a challenging problem due to large variation in the appearance and shapes of cells depending on many factors including imaging methodology, biological characteristics of cells, cell matrix composition, labeling methodology, etc. Often cell tracking methods require a sequence-specific segmentation method and manual tuning of many tracking parameters, which limits their applicability to sequences other than those they are designed for. In this paper, we propose 1) a deep learning based cell proposal method, which proposes candidates for cells along with their scores, and 2) a cell tracking method, which links proposals in adjacent frames in a graphical model using edges representing different cellular events and poses joint cell detection and tracking as the selection of a subset of cell and edge proposals. Our method is completely automated and given enough training data can be applied to a wide variety of microscopy sequences. We evaluate our method on multiple fluorescence and phase contrast microscopy sequences containing cells of various shapes and appearances from ISBI cell tracking challenge, and show that our method outperforms existing cell tracking methods. Code is available at: https://github.com/SaadUllahAkram/CellTracker},\n bibtype = {article},\n author = {Akram, Saad Ullah and Kannala, Juho and Eklund, Lauri and Heikkilä, Janne},\n journal = {arXiv preprint arXiv:1705.03386}\n}

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\n \n\n \n \n \n \n \n \n Inertial-based scale estimation for structure from motion on mobile devices.\n \n \n \n \n\n\n \n Mustaniemi, J.; Kannala, J.; Sarkka, S.; Matas, J.; and Heikkila, J.\n\n\n \n\n\n\n In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 4394-4401, 9 2017. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"Inertial-based$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Inertial-based scale estimation for structure from motion on mobile devices},\n type = {inproceedings},\n year = {2017},\n pages = {4394-4401},\n websites = {https://arxiv.org/abs/1611.09498},\n month = {9},\n publisher = {IEEE},\n id = {803cf770-8bc3-343b-ab51-1b06d100c8f5},\n created = {2019-09-15T16:34:29.864Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2020-09-23T11:35:23.119Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Mustaniemi2017},\n source_type = {CONF},\n folder_uuids = {67b5fbfc-903a-4f35-8d95-f58fc1430bfd},\n private_publication = {false},\n abstract = {Structure from motion algorithms have an inherent limitation that the reconstruction can only be determined up to the unknown scale factor. Modern mobile devices are equipped with an inertial measurement unit (IMU), which can be used for estimating the scale of the reconstruction. We propose a method that recovers the metric scale given inertial measurements and camera poses. In the process, we also perform a temporal and spatial alignment of the camera and the IMU. Therefore, our solution can be easily combined with any existing visual reconstruction software. The method can cope with noisy camera pose estimates, typically caused by motion blur or rolling shutter artifacts, via utilizing a Rauch-Tung-Striebel (RTS) smoother. Furthermore, the scale estimation is performed in the frequency domain, which provides more robustness to inaccurate sensor time stamps and noisy IMU samples than the previously used time domain representation. In contrast to previous methods, our approach has no parameters that need to be tuned for achieving a good performance. In the experiments, we show that the algorithm outperforms the state-of-the-art in both accuracy and convergence speed of the scale estimate. The accuracy of the scale is around $1\\%$ from the ground truth depending on the recording. We also demonstrate that our method can improve the scale accuracy of the Project Tango's build-in motion tracking.},\n bibtype = {inproceedings},\n author = {Mustaniemi, Janne and Kannala, Juho and Sarkka, Simo and Matas, Jiri and Heikkila, Janne},\n doi = {10.1109/IROS.2017.8206303},\n booktitle = {2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}\n}

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\n \n\n \n \n \n \n \n \n Video Summarization Using Deep Semantic Features.\n \n \n \n \n\n\n \n Otani, M.; Nakashima, Y.; Rahtu, E.; Heikkilä, J.; and Yokoya, N.\n\n\n \n\n\n\n In Computer Vision – ACCV 2016. ACCV 2016. Lecture Notes in Computer Science, volume 10115 LNCS, pages 361-377, 2017. Springer, Cham\n \n\n\n\n
\n\n\n\n \n \n $\"Video$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Video Summarization Using Deep Semantic Features},\n type = {inproceedings},\n year = {2017},\n pages = {361-377},\n volume = {10115 LNCS},\n websites = {http://link.springer.com/10.1007/978-3-319-54193-8_23},\n publisher = {Springer, Cham},\n id = {2d2e2f1d-9137-3b77-955d-10abdd2428ba},\n created = {2019-09-15T16:34:29.922Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-11-20T19:31:02.511Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Otani2017},\n source_type = {CONF},\n private_publication = {false},\n abstract = {This paper presents a video summarization technique for an Internet video to provide a quick way to overview its content. This is a challenging problem because finding important or informative parts of the original video requires to understand its content. Furthermore the content of Internet videos is very diverse, ranging from home videos to documentaries, which makes video summarization much more tough as prior knowledge is almost not available. To tackle this problem, we propose to use deep video features that can encode various levels of content semantics, including objects, actions, and scenes, improving the efficiency of standard video summarization techniques. For this, we design a deep neural network that maps videos as well as descriptions to a common semantic space and jointly trained it with associated pairs of videos and descriptions. To generate a video summary, we extract the deep features from each segment of the original video and apply a clustering-based summarization technique to them. We evaluate our video summaries using the SumMe dataset as well as baseline approaches. The results demonstrated the advantages of incorporating our deep semantic features in a video summarization technique.},\n bibtype = {inproceedings},\n author = {Otani, Mayu and Nakashima, Yuta and Rahtu, Esa and Heikkilä, Janne and Yokoya, Naokazu},\n doi = {10.1007/978-3-319-54193-8_23},\n booktitle = {Computer Vision – ACCV 2016. ACCV 2016. Lecture Notes in Computer Science}\n}

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\n \n 2016\n \n \n (10)\n \n \n

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\n \n\n \n \n \n \n \n \n MORE – a multimodal observation and analysis system for social interaction research.\n \n \n \n \n\n\n \n Keskinarkaus, A.; Huttunen, S.; Siipo, A.; Holappa, J.; Laszlo, M.; Juuso, I.; Väyrynen, E.; Heikkilä, J.; Lehtihalmes, M.; Seppänen, T.; and Laukka, S.\n\n\n \n\n\n\n Multimedia Tools and Applications, 75(11): 6321-6345. 6 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"MORE$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {MORE – a multimodal observation and analysis system for social interaction research},\n type = {article},\n year = {2016},\n keywords = {Audio,Collaboration,Computer vision,Database,Metadata,Social interaction,Speech analysis,Spherical video,Web technologies},\n pages = {6321-6345},\n volume = {75},\n websites = {http://link.springer.com/10.1007/s11042-015-2574-9},\n month = {6},\n day = {2},\n id = {be765e89-0fef-3706-a900-a81c78cf4d35},\n created = {2019-09-15T16:34:27.795Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-23T18:20:08.643Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {JOUR},\n private_publication = {false},\n abstract = {The MORE system is designed for observation and machine-aided analysis\\nof social interaction in real life situations, such as classroom\\nteaching scenarios and business meetings. The system utilizes a\\nmultichannel approach to collect data whereby multiple streams of data\\nin a number of different modalities are obtained from each situation.\\nTypically the system collects a 360-degree video and audio feed from\\nmultiple microphones set up in the space. The system includes an\\nadvanced server backend component that is capable of performing video\\nprocessing, feature extraction and archiving operations on behalf of the\\nuser. The feature extraction services form a key part of the system and\\nrely on advanced signal analysis techniques, such as speech processing,\\nmotion activity detection and facial expression recognition in order to\\nspeed up the analysis of large data sets. The provided web interface\\nweaves the multiple streams of information together, utilizes the\\nextracted features as metadata on the audio and video data and lets the\\nuser dive into analyzing the recorded events. The objective of the\\nsystem is to facilitate easy navigation of multimodal data and enable\\nthe analysis of the recorded situations for the purposes of, for\\nexample, behavioral studies, teacher training and business development.\\nA further unique feature of the system is its low setup overhead and\\nhigh portability as the lightest MORE setup only requires a laptop\\ncomputer and the selected set of sensors on site.},\n bibtype = {article},\n author = {Keskinarkaus, Anja and Huttunen, Sami and Siipo, Antti and Holappa, Jukka and Laszlo, Magda and Juuso, Ilkka and Väyrynen, Eero and Heikkilä, Janne and Lehtihalmes, Matti and Seppänen, Tapio and Laukka, Seppo},\n doi = {10.1007/s11042-015-2574-9},\n journal = {Multimedia Tools and Applications},\n number = {11}\n}

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\n \n\n \n \n \n \n \n \n Geometry based exhaustive line correspondence determination.\n \n \n \n \n\n\n \n Srikrishna, B., K., K.; Musti, U.; and Heikkila, J.\n\n\n \n\n\n\n In 2016 IEEE International Conference on Robotics and Automation (ICRA), pages 4341-4348, 5 2016. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"Geometry$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Geometry based exhaustive line correspondence determination},\n type = {inproceedings},\n year = {2016},\n pages = {4341-4348},\n websites = {http://ieeexplore.ieee.org/document/7487633/},\n month = {5},\n publisher = {IEEE},\n id = {f815fee6-ec4a-3edc-91d9-e5307e906513},\n created = {2019-09-15T16:34:29.860Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-23T18:20:07.960Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {In this paper we propose a purely geometric approach to establish correspondence between 3D line segments in a given model and 2D line segments detected in an image. Contrary to the existing methods which use strong assumptions on camera pose, we perform exhaustive search in order to compute maximum number of geometrically permitted correspondences between a 3D model and 2D lines. We present a novel theoretical framework in which we sample the space of camera axis direction (which is bounded and hence can be densely sampled unlike the unbounded space of camera position) and show that geometric constraints arising from it reduce rest of the computation to simple operations of finding camera position as the intersection of 3 planes. These geometric constraints can be represented using indexed arrays which accelerate it further. The algorithm returns all sets of correspondences and associated camera poses having high geometric consensus. The obtained experimental results show that our method has better asymptotic behavior than conventional approach. We also show that with the inclusion of additional sensor information our method can be used to initialize pose in just few seconds in many practical situations.},\n bibtype = {inproceedings},\n author = {Srikrishna, Bhat K K and Musti, Utpala and Heikkila, Janne},\n doi = {10.1109/ICRA.2016.7487633},\n booktitle = {2016 IEEE International Conference on Robotics and Automation (ICRA)}\n}

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\n \n\n \n \n \n \n \n Transfer learning for cell nuclei classification in histopathology images.\n \n \n \n\n\n \n Bayramoglu, N.; and Heikkilä, J.\n\n\n \n\n\n\n In Computer Vision – ECCV 2016 Workshops. ECCV 2016. Lecture Notes in Computer Science, volume 9915 LNCS, pages 532-539, 2016. Springer, Cham\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Transfer learning for cell nuclei classification in histopathology images},\n type = {inproceedings},\n year = {2016},\n pages = {532-539},\n volume = {9915 LNCS},\n publisher = {Springer, Cham},\n id = {1235f96d-4649-30d9-8e06-f9ee501f54e3},\n created = {2019-09-15T16:34:29.964Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-16T06:31:00.889Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {In histopathological image assessment, there is a high de-mand to obtain fast and precise quantification automatically. Such au-tomation could be beneficial to find clinical assessment clues to produce correct diagnoses, to reduce observer variability, and to increase objec-tivity. Due to its success in other areas, deep learning could be the key method to obtain clinical acceptance. However, the major bottleneck is how to train a deep CNN model with a limited amount of training data. There is one important question of critical importance: Could it be possi-ble to use transfer learning and fine-tuning in biomedical image analysis to reduce the effort of manual data labeling and still obtain a full deep representation for the target task? In this study, we address this ques-tion quantitatively by comparing the performances of transfer learning and learning from scratch for cell nuclei classification. We evaluate four different CNN architectures trained on natural images and facial images.},\n bibtype = {inproceedings},\n author = {Bayramoglu, Neslihan and Heikkilä, Janne},\n doi = {10.1007/978-3-319-49409-8_46},\n booktitle = {Computer Vision – ECCV 2016 Workshops. ECCV 2016. Lecture Notes in Computer Science}\n}

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\n \n\n \n \n \n \n \n \n Deep learning for magnification independent breast cancer histopathology image classification.\n \n \n \n \n\n\n \n Bayramoglu, N.; Kannala, J.; and Heikkila, J.\n\n\n \n\n\n\n In 2016 23rd International Conference on Pattern Recognition (ICPR), pages 2440-2445, 12 2016. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"Deep$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Deep learning for magnification independent breast cancer histopathology image classification},\n type = {inproceedings},\n year = {2016},\n pages = {2440-2445},\n websites = {http://ieeexplore.ieee.org/document/7900002/},\n month = {12},\n publisher = {IEEE},\n id = {acb38585-4469-3660-b99b-79fa8e7b1af9},\n created = {2019-09-15T16:34:29.968Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-23T18:20:08.096Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {—Microscopic analysis of breast tissues is necessary for a definitive diagnosis of breast cancer which is the most common cancer among women. Pathology examination requires time consuming scanning through tissue images under different magnification levels to find clinical assessment clues to produce correct diagnoses. Advances in digital imaging techniques offers assessment of pathology images using computer vision and machine learning methods which could automate some of the tasks in the diagnostic pathology workflow. Such automation could be beneficial to obtain fast and precise quantification, reduce observer variability, and increase objectivity. In this work, we propose to classify breast cancer histopathol-ogy images independent of their magnifications using convo-lutional neural networks (CNNs). We propose two different architectures; single task CNN is used to predict malignancy and multi-task CNN is used to predict both malignancy and image magnification level simultaneously. Evaluations and comparisons with previous results are carried out on BreaKHis dataset. Experimental results show that our magnification independent CNN approach improved the performance of magnification specific model. Our results in this limited set of training data are comparable with previous state-of-the-art results obtained by hand-crafted features. However, unlike previous methods, our approach has potential to directly benefit from additional training data, and such additional data could be captured with same or different magnification levels than previous data.},\n bibtype = {inproceedings},\n author = {Bayramoglu, Neslihan and Kannala, Juho and Heikkila, Janne},\n doi = {10.1109/ICPR.2016.7900002},\n booktitle = {2016 23rd International Conference on Pattern Recognition (ICPR)}\n}

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\n \n\n \n \n \n \n \n \n Learning Joint Representations of Videos and Sentences with Web Image Search.\n \n \n \n \n\n\n \n Otani, M.; Nakashima, Y.; Rahtu, E.; Heikkilä, J.; and Yokoya, N.\n\n\n \n\n\n\n Volume 9913 LNCS . Computer Vision – ECCV 2016 Workshops. ECCV 2016. Lecture Notes in Computer Science, pages 651-667. Springer, Cham, 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Computer$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@inbook{\n type = {inbook},\n year = {2016},\n keywords = {Image search,Multimodal embedding,Neural network,Representation learning,Sentence retrieval,Video retrieval},\n pages = {651-667},\n volume = {9913 LNCS},\n websites = {http://link.springer.com/10.1007/978-3-319-46604-0_46},\n publisher = {Springer, Cham},\n id = {29cb8cf0-555d-3f73-8a90-a7be36cb7ab5},\n created = {2019-09-15T16:34:30.017Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-23T18:20:07.921Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {Our objective is video retrieval based on natural language queries. In addition, we consider the analogous problem of retrieving sentences or generating descriptions given an input video. Recent work has addressed the problem by embedding visual and textual inputs into a common space where semantic similarities correlate to distances. We also adopt the embedding approach, and make the following contributions: First, we utilize web image search in sentence embedding process to disambiguate fine-grained visual concepts. Second, we propose embedding models for sentence, image, and video inputs whose parameters are learned simultaneously. Finally, we show how the proposed model can be applied to description generation. Overall, we observe a clear improvement over the state-of-the-art methods in the video and sentence retrieval tasks. In description generation, the performance level is comparable to the current state-of-the-art, although our embeddings were trained for the retrieval tasks.},\n bibtype = {inbook},\n author = {Otani, Mayu and Nakashima, Yuta and Rahtu, Esa and Heikkilä, Janne and Yokoya, Naokazu},\n doi = {10.1007/978-3-319-46604-0_46},\n chapter = {Learning Joint Representations of Videos and Sentences with Web Image Search},\n title = {Computer Vision – ECCV 2016 Workshops. ECCV 2016. Lecture Notes in Computer Science}\n}

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\n \n\n \n \n \n \n \n \n Parallax correction via disparity estimation in a multi-aperture camera.\n \n \n \n \n\n\n \n Mustaniemi, J.; Kannala, J.; and Heikkilä, J.\n\n\n \n\n\n\n Machine Vision and Applications, 27(8): 1313-1323. 11 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Parallax$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Parallax correction via disparity estimation in a multi-aperture camera},\n type = {article},\n year = {2016},\n keywords = {Census transform,Graph cuts,Mutual information,Semi-global matching,Trifocal tensor},\n pages = {1313-1323},\n volume = {27},\n websites = {http://link.springer.com/10.1007/s00138-016-0773-7},\n month = {11},\n day = {25},\n id = {41d7a725-4a99-33ef-953a-8b04d7f776d7},\n created = {2019-09-15T16:34:30.066Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-23T18:20:08.647Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {JOUR},\n private_publication = {false},\n abstract = {In this paper, an image fusion algorithm is proposed for a multi-aperture camera. Such camera is a feasible alternative to traditional Bayer filter camera in terms of image quality, camera size and camera features. The camera consists of several camera units, each having dedicated optics and color filter. The main challenge of a multi-aperture camera arises from the fact that each camera unit has a slightly different viewpoint. Our image fusion algorithm corrects the parallax error between the sub-images using a disparity map, which is estimated from the single-spectral images. We improve the disparity estimation by combining matching costs over multiple views using trifocal tensors. Images are matched using two alternative matching costs, mutual information and Census transform. We also compare two different disparity estimation methods, graph cuts and semi-global matching. The results show that the overall quality of the fused images is near the reference images. © 2016, Springer-Verlag Berlin Heidelberg.},\n bibtype = {article},\n author = {Mustaniemi, Janne and Kannala, Juho and Heikkilä, Janne},\n doi = {10.1007/s00138-016-0773-7},\n journal = {Machine Vision and Applications},\n number = {8}\n}

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\n \n\n \n \n \n \n \n \n Joint cell segmentation and tracking using cell proposals.\n \n \n \n \n\n\n \n Akram, S., U.; Kannala, J.; Eklund, L.; and Heikkila, J.\n\n\n \n\n\n\n In 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), pages 920-924, 4 2016. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"Joint$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@inproceedings{\n title = {Joint cell segmentation and tracking using cell proposals},\n type = {inproceedings},\n year = {2016},\n keywords = {cell proposals,cell segmentation,cell tracking,joint segmentation and tracking},\n pages = {920-924},\n websites = {http://ieeexplore.ieee.org/document/7493415/},\n month = {4},\n publisher = {IEEE},\n id = {892a3ec2-03de-37c1-bcbe-ecfa6cc63f83},\n created = {2019-09-15T16:34:30.112Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-23T18:20:08.293Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {Time-lapse microscopy imaging has advanced rapidly in last few decades and is producing large volume of data in cell and developmental biology. This has increased the importance of automated analyses, which depend heavily on cell segmen- tation and tracking as these are the initial stages when com- puting most biologically important cell properties. In this pa- per, we propose a novel joint cell segmentation and tracking method for fluorescence microscopy sequences, which gen- erates a large set of cell proposals, creates a graph represent- ing different cell events and then iteratively finds the most probable path within this graph providing cell segmentations and tracks. We evaluate our method on three datasets from ISBI Cell Tracking Challenge and show that our greedy non- optimal joint solution results in improved performance com- pared with state of the art methods. Index},\n bibtype = {inproceedings},\n author = {Akram, Saad Ullah and Kannala, Juho and Eklund, Lauri and Heikkila, Janne},\n doi = {10.1109/ISBI.2016.7493415},\n booktitle = {2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)}\n}

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\n \n\n \n \n \n \n \n Guest Editorial: Immersive Audio/Visual Systems.\n \n \n \n\n\n \n Xie, L.; Wang, L.; Heikkilä, J.; and Zhang, P.\n\n\n \n\n\n\n Multimedia Tools and Applications, 75(9): 5047-5053. 2016.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{\n title = {Guest Editorial: Immersive Audio/Visual Systems},\n type = {article},\n year = {2016},\n pages = {5047-5053},\n volume = {75},\n id = {a424132f-ca90-37cd-bb94-a6cfce6bfc1f},\n created = {2019-09-15T16:34:30.120Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-16T06:31:00.890Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {JOUR},\n private_publication = {false},\n bibtype = {article},\n author = {Xie, Lei and Wang, Longbiao and Heikkilä, Janne and Zhang, Peng},\n journal = {Multimedia Tools and Applications},\n number = {9}\n}

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\n \n\n \n \n \n \n \n \n Cell proposal network for microscopy image analysis.\n \n \n \n \n\n\n \n Akram, S., U.; Kannala, J.; Eklund, L.; and Heikkila, J.\n\n\n \n\n\n\n In 2016 IEEE International Conference on Image Processing (ICIP), pages 3199-3203, 9 2016. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"Cell$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@inproceedings{\n title = {Cell proposal network for microscopy image analysis},\n type = {inproceedings},\n year = {2016},\n keywords = {Cell detection,Cell proposals,Cell tracking,Deep learning,Fully convolutional network},\n pages = {3199-3203},\n websites = {http://ieeexplore.ieee.org/document/7532950/},\n month = {9},\n publisher = {IEEE},\n id = {e9895429-7f17-3b92-b0b6-9f6e2f7c840e},\n created = {2019-09-19T17:36:36.944Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-23T18:20:08.837Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {Accurate cell segmentation is vital for the development of re-liable microscopy image analysis methods. It is a very challenging prob-lem due to low contrast, weak boundaries, and conjoined and overlapping cells; producing many ambiguous regions, which lower the performance of automated segmentation methods. Cell proposals provide an efficient way of exploiting both spatial and temporal context, which can be very helpful in many of these ambiguous regions. However, most proposal based microscopy image analysis methods rely on fairly simple proposal generation stage, limiting their performance. In this paper, we propose a convolutional neural network based method which provides cell seg-mentation proposals, which can be used for cell detection, segmentation and tracking. We evaluate our method on datasets from histology, flu-orescence and phase contrast microscopy and show that it outperforms state of the art cell detection and segmentation methods.},\n bibtype = {inproceedings},\n author = {Akram, Saad Ullah and Kannala, Juho and Eklund, Lauri and Heikkila, Janne},\n doi = {10.1109/ICIP.2016.7532950},\n booktitle = {2016 IEEE International Conference on Image Processing (ICIP)}\n}

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\n \n\n \n \n \n \n \n Forget the checkerboard: Practical self-calibration using a planar scene.\n \n \n \n\n\n \n Herrera, D.; Kannala, C.; and Heikkila, J.\n\n\n \n\n\n\n In 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016, 2016. \n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Forget the checkerboard: Practical self-calibration using a planar scene},\n type = {inproceedings},\n year = {2016},\n id = {a0d0ed47-1c00-3500-b4a6-a9ddcde4d54d},\n created = {2019-11-14T11:05:18.645Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-11-14T11:05:18.645Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n private_publication = {true},\n abstract = {© 2016 IEEE. We introduce a camera self-calibration method using a planar scene of unknown texture. Planar surfaces are everywhere but checkerboards are not, thus the method can be more easily applied outside the lab. We demonstrate that the accuracy is equivalent to a checkerboard-based calibration, so there is no need for printing checkerboards any more. Moreover, the use of a planar scene provides improved robustness and stronger constraints than a self-calibration with an arbitrary scene. We utilize a closed-form initialization of the focal length with minimal and practical assumptions. The method recovers the intrinsic and extrinsic parameters of the camera and the metric structure of the planar scene. The method is implemented in a real-time application for non-expert users that provides an easy and practical process to obtain high accuracy calibrations.},\n bibtype = {inproceedings},\n author = {Herrera, D. and Kannala, C.J. and Heikkila, J.},\n doi = {10.1109/WACV.2016.7477641},\n booktitle = {2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016}\n}

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\n \n\n \n \n \n \n \n \n Focal length change compensation for monocular slam.\n \n \n \n \n\n\n \n Taketomi, T.; and Heikkila, J.\n\n\n \n\n\n\n In 2015 IEEE International Conference on Image Processing (ICIP), pages 4982-4986, 9 2015. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"Focal$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

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@inproceedings{\n title = {Focal length change compensation for monocular slam},\n type = {inproceedings},\n year = {2015},\n keywords = {Augmented Reality,Camera Zoom,SLAM},\n pages = {4982-4986},\n websites = {http://ieeexplore.ieee.org/document/7351755/},\n month = {9},\n publisher = {IEEE},\n id = {8891eec6-b301-38ec-b94b-54796b20dbc0},\n created = {2019-09-15T16:34:27.563Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-23T18:20:08.467Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {© 2015 IEEE. In this paper, we propose a method for handling focal length changes in the SLAM algorithm. Our method is designed as a pre-processing step to first estimate the change of the camera focal length, and then compensate for the zooming effects before running the actual SLAM algorithm. By using our method, camera zooming can be used in the existing SLAM algorithms with minor modifications. In the experiments, the effectiveness of the proposed method was quantitatively evaluated. The results indicate that the method can successfully deal with abrupt changes of the camera focal length.},\n bibtype = {inproceedings},\n author = {Taketomi, Takafumi and Heikkila, Janne},\n doi = {10.1109/ICIP.2015.7351755},\n booktitle = {2015 IEEE International Conference on Image Processing (ICIP)}\n}

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\n \n\n \n \n \n \n \n \n Human Epithelial Type 2 cell classification with convolutional neural networks.\n \n \n \n \n\n\n \n Bayramoglu, N.; Kannala, J.; and Heikkila, J.\n\n\n \n\n\n\n In 2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE), pages 1-6, 11 2015. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"Human$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Human Epithelial Type 2 cell classification with convolutional neural networks},\n type = {inproceedings},\n year = {2015},\n pages = {1-6},\n websites = {http://ieeexplore.ieee.org/document/7367705/},\n month = {11},\n publisher = {IEEE},\n id = {10ee26ec-ee87-33e5-ba58-a4e71777b9b5},\n created = {2019-09-15T16:34:27.597Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-23T18:20:08.485Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Bayramoglu, Neslihan and Kannala, Juho and Heikkila, Janne},\n doi = {10.1109/BIBE.2015.7367705},\n booktitle = {2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE)}\n}

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\n \n\n \n \n \n \n \n \n Automated tracking of tumor-stroma morphology in microtissues identifies functional targets within the tumor microenvironment for therapeutic intervention.\n \n \n \n \n\n\n \n Åkerfelt, M.; Bayramoglu, N.; Robinson, S.; Toriseva, M.; Schukov, H.; Härmä, V.; Virtanen, J.; Sormunen, R.; Kaakinen, M.; Kannala, J.; Eklund, L.; Heikkilä, J.; and Nees, M.\n\n\n \n\n\n\n Oncotarget, 6(30): 30035-30056. 10 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Automated$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Automated tracking of tumor-stroma morphology in microtissues identifies functional targets within the tumor microenvironment for therapeutic intervention},\n type = {article},\n year = {2015},\n keywords = {3D co-culture,Cancer associated fibroblast (CAF),Focal adhesion kinase (FAK),Invasion,Phenotypic screening},\n pages = {30035-30056},\n volume = {6},\n websites = {http://www.oncotarget.com/fulltext/5046},\n month = {10},\n day = {6},\n id = {4a8016e9-7f12-338f-8660-9c6b9757bdf8},\n created = {2019-09-15T16:34:27.601Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-23T18:20:08.027Z},\n read = {true},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {JOUR},\n private_publication = {false},\n abstract = {Cancer-associated fibroblasts (CAFs) constitute an important part of the tumor microenvironment and promote invasion via paracrine functions and physical impact on the tumor. Although the importance of including CAFs into three-dimensional (3D) cell cultures has been acknowledged, computational support for quantitative live-cell measurements of complex cell cultures has been lacking. Here, we have developed a novel automated pipeline to model tumor-stroma interplay, track motility and quantify morphological changes of 3D co-cultures, in real-time live-cell settings. The platform consists of microtissues from prostate cancer cells, combined with CAFs in extracellular matrix that allows biochemical perturbation. Tracking of fibroblast dynamics revealed that CAFs guided the way for tumor cells to invade and increased the growth and invasiveness of tumor organoids. We utilized the platform to determine the efficacy of inhibitors in prostate cancer and the associated tumor microenvironment as a functional unit. Interestingly, certain inhibitors selectively disrupted tumor-CAF interactions, e.g. focal adhesion kinase (FAK) inhibitors specifically blocked tumor growth and invasion concurrently with fibroblast spreading and motility. This complex phenotype was not detected in other standard in vitro models. These results highlight the advantage of our approach, which recapitulates tumor histology and can significantly improve cancer target validation in vitro.},\n bibtype = {article},\n author = {Åkerfelt, Malin and Bayramoglu, Neslihan and Robinson, Sean and Toriseva, Mervi and Schukov, Hannu-Pekka and Härmä, Ville and Virtanen, Johannes and Sormunen, Raija and Kaakinen, Mika and Kannala, Juho and Eklund, Lauri and Heikkilä, Janne and Nees, Matthias},\n doi = {10.18632/oncotarget.5046},\n journal = {Oncotarget},\n number = {30}\n}

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\n \n\n \n \n \n \n \n \n Disparity Estimation for Image Fusion in a Multi-aperture Camera.\n \n \n \n \n\n\n \n Mustaniemi, J.; Kannala, J.; and Heikkilä, J.\n\n\n \n\n\n\n Volume 9257 . Computer Analysis of Images and Patterns. CAIP 2015. Lecture Notes in Computer Science, pages 158-170. Springer, Cham, 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Computer$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

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@inbook{\n type = {inbook},\n year = {2015},\n keywords = {Census transform,Mutual information,Trifocal tensor},\n pages = {158-170},\n volume = {9257},\n websites = {http://link.springer.com/10.1007/978-3-319-23117-4_14},\n publisher = {Springer, Cham},\n id = {3d2150d6-7dfe-39dc-bcae-02336bbe939f},\n created = {2019-09-15T16:34:27.633Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-23T18:20:08.124Z},\n read = {true},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {In this paper, an image fusion algorithm is proposed for a multi-aperture camera. Such camera is a worthy alternative to traditional Bayer filter camera in terms of image quality, camera size and camera features. The camera consists of several camera units, each having ded- icated optics and color filter. The main challenge of a multi-aperture camera arises from the fact that each camera unit has a slightly differ- ent viewpoint. Our image fusion algorithm corrects the parallax error between the sub-images using a disparity map, which is estimated from the multi-spectral images.We improve the disparity estimation by combining matching costs over multiple views with help of trifocal tensors. Images are matched using two alternative matching costs, mutual information andCensus transform.We also compare two different disparity estimation methods, graph cuts and semi-globalmatching. The results show that the overall quality of the fused images is near the reference images.},\n bibtype = {inbook},\n author = {Mustaniemi, Janne and Kannala, Juho and Heikkilä, Janne},\n doi = {10.1007/978-3-319-23117-4_14},\n chapter = {Disparity Estimation for Image Fusion in a Multi-aperture Camera},\n title = {Computer Analysis of Images and Patterns. CAIP 2015. Lecture Notes in Computer Science}\n}

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\n \n\n \n \n \n \n \n \n Optimizing the Accuracy and Compactness of Multi-view Reconstructions.\n \n \n \n \n\n\n \n Ylimäki, M.; Kannala, J.; and Heikkilä, J.\n\n\n \n\n\n\n Volume 9257 . Computer Analysis of Images and Patterns. CAIP 2015. Lecture Notes in Computer Science, pages 171-183. Springer, Cham, 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Computer$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

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@inbook{\n type = {inbook},\n year = {2015},\n keywords = {Compactness-accuracy trade-off,Mesh optimization,Multi-view stereo evaluation},\n pages = {171-183},\n volume = {9257},\n websites = {http://link.springer.com/10.1007/978-3-319-23117-4_15},\n publisher = {Springer, Cham},\n id = {cd5af6c4-785b-398c-84fa-fe9a3e4e3c93},\n created = {2019-09-15T16:34:27.637Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-23T18:20:07.818Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n bibtype = {inbook},\n author = {Ylimäki, Markus and Kannala, Juho and Heikkilä, Janne},\n doi = {10.1007/978-3-319-23117-4_15},\n chapter = {Optimizing the Accuracy and Compactness of Multi-view Reconstructions},\n title = {Computer Analysis of Images and Patterns. CAIP 2015. Lecture Notes in Computer Science}\n}

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\n \n\n \n \n \n \n \n \n A novel feature descriptor based on microscopy image statistics.\n \n \n \n \n\n\n \n Bayramoglu, N.; Kannala, J.; Akerfelt, M.; Kaakinen, M.; Eklund, L.; Nees, M.; and Heikkila, J.\n\n\n \n\n\n\n In 2015 IEEE International Conference on Image Processing (ICIP), pages 2695-2699, 9 2015. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@inproceedings{\n title = {A novel feature descriptor based on microscopy image statistics},\n type = {inproceedings},\n year = {2015},\n keywords = {cell co-culture,cell detection,electron microscopy,local image descriptor,mitochondria,phase contrast imaging,pixel labeling,tumor},\n pages = {2695-2699},\n websites = {http://ieeexplore.ieee.org/document/7351292/},\n month = {9},\n publisher = {IEEE},\n id = {06f66f46-06e4-3fb5-b62e-afaa9ebfaffe},\n created = {2019-09-15T16:34:27.675Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-23T18:20:08.641Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {© 2015 IEEE. In this paper, we propose a novel feature description algorithm based on image statistics. The pipeline first performs independent component analysis on training image patches to obtain basis vectors (filters) for a lower dimensional representation. Then for a given image, a set of filter responses at each pixel is computed. Finally, a histogram representation, which considers the signs and magnitudes of the responses as well as the number of filters, is applied on local image patches. We propose to apply this idea to a microscopy image pixel identification system based on a learning framework. Experimental results show that the proposed algorithm performs better than the state-of-the-art descriptors in biomedical images of different microscopy modalities.},\n bibtype = {inproceedings},\n author = {Bayramoglu, Neslihan and Kannala, Juho and Akerfelt, Malin and Kaakinen, Mika and Eklund, Lauri and Nees, Matthias and Heikkila, Janne},\n doi = {10.1109/ICIP.2015.7351292},\n booktitle = {2015 IEEE International Conference on Image Processing (ICIP)}\n}

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\n \n\n \n \n \n \n \n \n Zoom factor compensation for monocular SLAM.\n \n \n \n \n\n\n \n Taketomi, T.; and Heikkila, J.\n\n\n \n\n\n\n In 2015 IEEE Virtual Reality (VR), pages 293-294, 3 2015. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"Zoom$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@inproceedings{\n title = {Zoom factor compensation for monocular SLAM},\n type = {inproceedings},\n year = {2015},\n keywords = {H.5.1 [Multimedia Information Systems]: Artificial,I.4.1 [Digitization and Image Capture]: Imaging ge,and virtual realities -,augmented},\n pages = {293-294},\n websites = {http://ieeexplore.ieee.org/document/7223411/},\n month = {3},\n publisher = {IEEE},\n id = {354ae6a6-3be8-35a5-9496-e77c6935abab},\n created = {2019-09-15T16:34:27.710Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-23T18:20:08.319Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {© 2015 IEEE. SLAM algorithms are widely used in augmented reality applications for registering virtual objects. Most SLAM algorithms estimate camera poses and 3D positions of feature points using known intrinsic camera parameters that are calibrated and fixed in advance. This assumption means that the algorithm does not allow changing the intrinsic camera parameters during runtime. We propose a method for handling focal length changes in the SLAM algorithm. Our method is designed as a pre-processing step for the SLAM algorithm input. In our method, the change of the focal length is estimated before the tracking process of the SLAM algorithm. Camera zooming effects in the input camera images are compensated for by using the estimated focal length change. By using our method, camera zooming can be used in the existing SLAM algorithms such as PTAM [4] with minor modifications. In the experiment, the effectiveness of the proposed method was quantitatively evaluated. The results indicate that the method can successfully deal with abrupt changes of the camera focal length.},\n bibtype = {inproceedings},\n author = {Taketomi, Takafumi and Heikkila, Janne},\n doi = {10.1109/VR.2015.7223411},\n booktitle = {2015 IEEE Virtual Reality (VR)}\n}

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\n \n\n \n \n \n \n \n \n Segmentation of Cells from Spinning Disk Confocal Images Using a Multi-stage Approach.\n \n \n \n \n\n\n \n Akram, S., U.; Kannala, J.; Kaakinen, M.; Eklund, L.; and Heikkilä, J.\n\n\n \n\n\n\n Volume 9005 . Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science, pages 300-314. Springer, Cham, 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Computer$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inbook{\n type = {inbook},\n year = {2015},\n pages = {300-314},\n volume = {9005},\n websites = {http://link.springer.com/10.1007/978-3-319-16811-1_20},\n publisher = {Springer, Cham},\n id = {45e6e301-3651-3a85-8fce-a5b323edd067},\n created = {2019-09-15T16:34:27.750Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-23T18:20:08.825Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n bibtype = {inbook},\n author = {Akram, Saad Ullah and Kannala, Juho and Kaakinen, Mika and Eklund, Lauri and Heikkilä, Janne},\n doi = {10.1007/978-3-319-16811-1_20},\n chapter = {Segmentation of Cells from Spinning Disk Confocal Images Using a Multi-stage Approach},\n title = {Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science}\n}

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\n \n\n \n \n \n \n \n \n Analysis of Sampling Techniques for Learning Binarized Statistical Image Features Using Fixations and Salience.\n \n \n \n \n\n\n \n Tavakoli, H., R.; Rahtu, E.; and Heikkilä, J.\n\n\n \n\n\n\n Volume 8926 . Computer Vision - ECCV 2014 Workshops. ECCV 2014. Lecture Notes in Computer Science, pages 124-134. Springer, Cham, 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Computer$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

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@inbook{\n type = {inbook},\n year = {2015},\n keywords = {Binary operators,Salience modeling,Visual attention},\n pages = {124-134},\n volume = {8926},\n websites = {http://link.springer.com/10.1007/978-3-319-16181-5_9},\n publisher = {Springer, Cham},\n id = {70c2df77-678e-3e5a-a06c-1bf4567328ea},\n created = {2019-09-15T16:34:27.759Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-23T18:20:07.960Z},\n read = {true},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {© Springer International Publishing Switzerland 2015. This paper studies the role of different sampling techniques in the process of learning Binarized Statistical Image Features (BSIF). It considers various sampling approaches including random sampling and selective sampling. The selective sampling utilizes either human eye tracking data or artificially generated fixations. To generate artificial fixations, this paper exploits salience models which apply to key point localization. Therefore, it proposes a framework grounded on the hypothesis that the most salient point conveys important information. Furthermore, it investigates possible performance gain by training BSIF filters on class specific data. To summarize, the contribution of this paper are as follows: 1) it studies different sampling strategies to learn BSIF filters, 2) it employs human fixations in the design of a binary operator, 3) it proposes an attention model to replicate human fixations, and 4) it studies the performance of learning application specific BSIF filters using attention modeling.},\n bibtype = {inbook},\n author = {Tavakoli, Hamed Rezazadegan and Rahtu, Esa and Heikkilä, Janne},\n doi = {10.1007/978-3-319-16181-5_9},\n chapter = {Analysis of Sampling Techniques for Learning Binarized Statistical Image Features Using Fixations and Salience},\n title = {Computer Vision - ECCV 2014 Workshops. ECCV 2014. Lecture Notes in Computer Science}\n}

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\n \n\n \n \n \n \n \n \n Fast and accurate multi-view reconstruction by multi-stage prioritised matching.\n \n \n \n \n\n\n \n Kannala, J.; Ylimäki, M.; Brandt, S., S.; Holappa, J.; and Heikkilä, J.\n\n\n \n\n\n\n IET Computer Vision, 9(4): 576-587. 8 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Fast$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{\n title = {Fast and accurate multi-view reconstruction by multi-stage prioritised matching},\n type = {article},\n year = {2015},\n pages = {576-587},\n volume = {9},\n websites = {https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2014.0281},\n month = {8},\n day = {1},\n id = {e08e245c-4650-3923-9b9f-dbd8a6e81068},\n created = {2019-09-15T16:34:27.796Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-23T18:20:08.017Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {JOUR},\n private_publication = {false},\n abstract = {In this paper, we propose a multi-view stereo re-construction method which creates a three-dimensional point cloud of a scene from multiple calibrated im-ages captured from different viewpoints. The method is based on a prioritized match expansion technique, which starts from a sparse set of seed points, and it-eratively expands them into neighboring areas by us-ing multiple expansion stages. Each seed point rep-resents a surface patch and has a position and a sur-face normal vector. The location and surface normal of the seeds are optimized using a homography-based lo-cal image alignment. The propagation of seeds is per-formed in a prioritized order in which the most promis-ing seeds are expanded first and removed from the list of seeds. The first expansion stage proceeds until the list of seeds is empty. In the following expansion stages, the current reconstruction may be further expanded by finding new seeds near the boundaries of the current reconstruction. The prioritized expansion strategy al-lows efficient generation of accurate point clouds and our experiments show its benefits compared with non-prioritized expansion. In addition, a comparison to the widely used patch-based multi-view stereo software (PMVS) shows that our method is significantly faster and produces more accurate and complete reconstruc-tions.},\n bibtype = {article},\n author = {Kannala, Juho and Ylimäki, Markus and Brandt, Sami S and Holappa, Jukka and Heikkilä, Janne},\n doi = {10.1049/iet-cvi.2014.0281},\n journal = {IET Computer Vision},\n number = {4}\n}

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\n \n\n \n \n \n \n \n \n Quaternion Wiener Deconvolution for Noise Robust Color Image Registration.\n \n \n \n \n\n\n \n Pedone, M.; Bayro-Corrochano, E.; Flusser, J.; and Heikkila, J.\n\n\n \n\n\n\n IEEE Signal Processing Letters, 22(9): 1278-1282. 9 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Quaternion$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Quaternion Wiener Deconvolution for Noise Robust Color Image Registration},\n type = {article},\n year = {2015},\n keywords = {Clifford algebra,Wiener filter,multivector derivative,phase correlation,quaternion},\n pages = {1278-1282},\n volume = {22},\n websites = {http://ieeexplore.ieee.org/document/7029035/},\n month = {9},\n id = {dcade7d6-ec9b-3a11-80d0-a22fa972dd8a},\n created = {2019-09-15T16:34:27.876Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-23T18:20:07.778Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {JOUR},\n private_publication = {false},\n abstract = {In this letter, we propose a global method for registering color images with respect to translation. Our approach is based on the idea of representing translations as convolutions with unknown shifted delta functions, and performing Wiener deconvolution in order to recover the shift between two images. We then derive a quaternionic version of the Wiener deconvolution filter in order to register color images. The use of Wiener filter also allows us to explicitly take into account the effect of noise. We prove that the well-known algorithm of phase correlation is a special case of our method, and we experimentally demonstrate the advantages of our approach by comparing it to other known generalizations of the phase correlation algorithm.},\n bibtype = {article},\n author = {Pedone, Matteo and Bayro-Corrochano, Eduardo and Flusser, Jan and Heikkila, Janne},\n doi = {10.1109/LSP.2015.2398033},\n journal = {IEEE Signal Processing Letters},\n number = {9}\n}

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\n \n\n \n \n \n \n \n Registration of Images with N-Fold Dihedral Blur.\n \n \n \n\n\n \n Pedone, M.; Flusser, J.; and Heikkilä, J.\n\n\n \n\n\n\n IEEE Transactions on Image Processing, 24(3): 1036-1045. 2015.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Registration of Images with N-Fold Dihedral Blur},\n type = {article},\n year = {2015},\n keywords = {Image registration,N-fold rotational symmetry,blurred images,dihedral symmetry,phase correlation},\n pages = {1036-1045},\n volume = {24},\n id = {d02e5c86-3514-331c-a52e-54de160b6acc},\n created = {2019-09-15T16:34:27.884Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-15T17:27:45.381Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {JOUR},\n private_publication = {false},\n abstract = {In this paper, we extend our recent registration method designed specifically for registering blurred images. The original method works for unknown blurs, assuming the blurring point-spread function (PSF) exhibits an N-fold rotational symmetry. Here, we also generalize the theory to the case of dihedrally symmetric blurs, which are produced by the PSFs having both rotational and axial symmetries. Such kind of blurs are often found in unfocused images acquired by digital cameras, as in out-of-focus shots the PSF typically mimics the shape of the shutter aperture. This makes our registration algorithm particularly well-suited in applications where blurred image registration must be used as a preprocess step of an image fusion algorithm, and where common registration methods fail, due to the amount of blur. We demonstrate that the proposed method leads to an improvement of the registration performance, and we show its applicability to real images by providing successful examples of blurred image registration followed by depth-of-field extension and multichannel blind deconvolution.},\n bibtype = {article},\n author = {Pedone, Matteo and Flusser, Jan and Heikkilä, Janne},\n doi = {10.1109/TIP.2015.2390977},\n journal = {IEEE Transactions on Image Processing},\n number = {3}\n}

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\n \n\n \n \n \n \n \n DT-SLAM: Deferred triangulation for robust SLAM.\n \n \n \n\n\n \n Herrera C, D.; Kim, K.; Kannala, J.; Pulli, K.; and Heikkilä, J.\n\n\n \n\n\n\n In Proceedings - 2014 International Conference on 3D Vision, 3DV 2014, volume 1, pages 609-616, 2015. IEEE\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {DT-SLAM: Deferred triangulation for robust SLAM},\n type = {inproceedings},\n year = {2015},\n pages = {609-616},\n volume = {1},\n publisher = {IEEE},\n id = {87980d0a-cdd1-31c2-99ee-0391bba31532},\n created = {2019-09-15T16:34:27.923Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2021-04-25T11:28:56.551Z},\n read = {true},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n folder_uuids = {67b5fbfc-903a-4f35-8d95-f58fc1430bfd},\n private_publication = {false},\n abstract = {Obtaining a good baseline between different video frames is one of the key elements in vision-based monocular SLAM systems. However, if the video frames contain only a few 2D feature correspondences with a good baseline, or the camera only rotates without sufficient translation in the beginning, tracking and mapping becomes unstable. We introduce a real-time visual SLAM system that incrementally tracks individual 2D features, and estimates camera pose by using matched 2D features, regardless of the length of the baseline. Triangulating 2D features into 3D points is deferred until keyframes with sufficient baseline for the features are available. Our method can also deal with pure rotational motions, and fuse the two types of measurements in a bundle adjustment step. Adaptive criteria for keyframe selection are also introduced for efficient optimization and dealing with multiple maps. We demonstrate that our SLAM system improves camera pose estimates and robustness, even with purely rotational motions.},\n bibtype = {inproceedings},\n author = {Herrera C, Daniel and Kim, Kihwan and Kannala, Juho and Pulli, Kari and Heikkilä, Janne},\n doi = {10.1109/3DV.2014.49},\n booktitle = {Proceedings - 2014 International Conference on 3D Vision, 3DV 2014}\n}

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\n \n\n \n \n \n \n \n \n Correction: Predicting the Valence of a Scene from Observers' Eye Movements.\n \n \n \n \n\n\n \n R.-Tavakoli, H.; Atyabi, A.; Rantanen, A.; Laukka, S., J.; Nefti-Meziani, S.; and Heikkilä, J.\n\n\n \n\n\n\n PLOS ONE, 10(10): e0141174. 10 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Correction:$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{\n title = {Correction: Predicting the Valence of a Scene from Observers' Eye Movements},\n type = {article},\n year = {2015},\n pages = {e0141174},\n volume = {10},\n websites = {https://dx.plos.org/10.1371/journal.pone.0141174},\n month = {10},\n day = {15},\n id = {46dea6c2-62f9-3bac-b41f-9fb2ef23c226},\n created = {2019-09-23T18:20:07.545Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-23T18:20:07.545Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {JOUR},\n private_publication = {false},\n bibtype = {article},\n author = {R.-Tavakoli, Hamed and Atyabi, Adham and Rantanen, Antti and Laukka, Seppo J and Nefti-Meziani, Samia and Heikkilä, Janne},\n doi = {10.1371/journal.pone.0141174},\n journal = {PLOS ONE},\n number = {10}\n}

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\n \n\n \n \n \n \n \n 3D point representation for pose estimation: Accelerated SIFT vs ORB.\n \n \n \n\n\n \n Bhat, K.; Kannala, J.; and Heikkilä, J.\n\n\n \n\n\n\n Volume 9127 2015.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

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@book{\n title = {3D point representation for pose estimation: Accelerated SIFT vs ORB},\n type = {book},\n year = {2015},\n source = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},\n keywords = {3D point recognition,Augmented Reality,Interest Points},\n volume = {9127},\n id = {22f43436-e12b-33f7-a80a-45c7702c5b78},\n created = {2019-11-14T11:05:18.637Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-11-14T11:05:18.637Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n private_publication = {true},\n abstract = {© Springer International Publishing Switzerland 2015. Many novel local image descriptors (Random Ferns, ORB etc) are being proposed each year with claims of being as good as or superior to SIFT for representing point features. In this context we design a simple experimental framework to compare the performances of different descriptors for realtime recognition of 3D points in a given environment. We use this framework to show that robust descriptors like SIFT perform far better when compared to fast binary descriptors like ORB if matching process uses approximate nearest-neighbor search (ANNS) for acceleration. Such an analysis can be very useful for making appropriate choice from vast number of descriptors available in the literature.We further apply machine learning techniques to obtain better approximation of SIFT descriptor matching than ANNS. Though we could not improve its performance, our in-depth analysis of its root cause provides useful insights for guiding future exploration in this topic.},\n bibtype = {book},\n author = {Bhat, K.K.S. and Kannala, J. and Heikkilä, J.},\n doi = {10.1007/978-3-319-19665-7_7}\n}

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\n \n\n \n \n \n \n \n Predicting the valence of a scene from observers' eye movements.\n \n \n \n\n\n \n Tavakoli, H.; Atyabi, A.; Rantanen, A.; Laukka, S.; Nefti-Meziani, S.; and Heikkilä, J.\n\n\n \n\n\n\n PLoS ONE, 10(9). 2015.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{\n title = {Predicting the valence of a scene from observers' eye movements},\n type = {article},\n year = {2015},\n volume = {10},\n id = {cb4a4cdc-d7c8-3363-9746-350f89b0fb65},\n created = {2019-11-14T11:05:18.705Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-11-14T11:05:18.705Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n private_publication = {true},\n abstract = {© 2015 R.-Tavakoli et al. Multimedia analysis benefits from understanding the emotional content of a scene in a variety of tasks such as video genre classification and content-based image retrieval. Recently, there has been an increasing interest in applying human bio-signals, particularly eye movements, to recognize the emotional gist of a scene such as its valence. In order to determine the emotional category of images using eye movements, the existing methods often learn a classifier using several features that are extracted from eye movements. Although it has been shown that eye movement is potentially useful for recognition of scene valence, the contribution of each feature is not well-studied. To address the issue, we study the contribution of features extracted from eye movements in the classification of images into pleasant, neutral, and unpleasant categories. We assess ten features and their fusion. The features are histogram of saccade orientation, histogram of saccade slope, histogram of saccade length, histogram of saccade duration, histogram of saccade velocity, histogram of fixation duration, fixation histogram, top-ten salient coordinates, and saliency map. We utilize machine learning approach to analyze the performance of features by learning a support vector machine and exploiting various feature fusion schemes. The experiments reveal that 'saliency map', 'fixation histogram', 'histogram of fixation duration', and 'histogram of saccade slope' are the most contributing features. The selected features signify the influence of fixation information and angular behavior of eye movements in the recognition of the valence of images.},\n bibtype = {article},\n author = {Tavakoli, H.R. and Atyabi, A. and Rantanen, A. and Laukka, S.J. and Nefti-Meziani, S. and Heikkilä, J.},\n doi = {10.1371/journal.pone.0138198},\n journal = {PLoS ONE},\n number = {9}\n}

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\n \n 2014\n \n \n (5)\n \n \n

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\n \n\n \n \n \n \n \n Emotional valence recognition, analysis of salience and eye movements.\n \n \n \n\n\n \n Tavakoli, H., R.; Yanulevskaya, V.; Rahtu, E.; Heikkilä, J.; and Sebe, N.\n\n\n \n\n\n\n In Proceedings - International Conference on Pattern Recognition, pages 4666-4671, 2014. IEEE\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@inproceedings{\n title = {Emotional valence recognition, analysis of salience and eye movements},\n type = {inproceedings},\n year = {2014},\n keywords = {Emotion,Eye movements,Saliency,Valence},\n pages = {4666-4671},\n publisher = {IEEE},\n id = {62283323-afa5-30ba-a361-e211ee6b09f6},\n created = {2019-09-15T16:34:27.838Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-19T17:47:33.116Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {© 2014 IEEE. This paper studies the performance of recorded eye movements and computational visual attention models (i.e. saliency models) in the recognition of emotional valence of an image. In the first part of this study, it employs eye movement data (fixation &amp; saccade) to build image content descriptors and use them with support vector machines to classify the emotional valence. In the second part, it examines if the human saliency map can be substituted with the state-of-the-art computational visual attention models in the task of valence recognition. The results indicate that the eye movement based descriptors provide significantly better performance compared to the baselines, which apply low-level visual cues (e.g. color, texture and shape). Furthermore, it will be shown that the current computational models for visual attention are not able to capture the emotional information in similar extent as the real eye movements.},\n bibtype = {inproceedings},\n author = {Tavakoli, Hamed R and Yanulevskaya, Victoria and Rahtu, Esa and Heikkilä, Janne and Sebe, Nicu},\n doi = {10.1109/ICPR.2014.798},\n booktitle = {Proceedings - International Conference on Pattern Recognition}\n}

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\n \n\n \n \n \n \n \n \n Line Matching and Pose Estimation for Unconstrained Model-to-Image Alignment.\n \n \n \n \n\n\n \n Bhat, K., K., S.; and Heikkila, J.\n\n\n \n\n\n\n In 2014 2nd International Conference on 3D Vision, volume 1, pages 155-162, 12 2014. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"Line$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Line Matching and Pose Estimation for Unconstrained Model-to-Image Alignment},\n type = {inproceedings},\n year = {2014},\n pages = {155-162},\n volume = {1},\n websites = {http://ieeexplore.ieee.org/document/7035821/},\n month = {12},\n publisher = {IEEE},\n id = {b1bd3870-a432-33bc-a6b5-e099bd45398a},\n created = {2019-09-15T16:34:27.838Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-23T18:20:07.967Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {This paper has two contributions in the context of line based camera pose estimation, 1) We propose a purely geometric approach to establish correspondence between 3D line segments in a given model and 2D line segments detected in an image, 2) We eliminate a degenerate case due to the type of rotation representation in arguably the best line based pose estimation method currently available. For establishing line correspondences we perform exhaustive search on the space of camera pose values till we obtain a pose (position and rotation) which is geometrically consistent with the given set of 2D, 3D lines. For this highly complex search we design a strategy which performs precomputations on the 3D model using separate set of constraints on position and rotation values. During runtime, the set of different rotation values are ranked independently and combined with each position values in the order of their ranking. Then successive geometric constraints which are much simpler when compared to computing reprojection error are used to eliminate incorrect pose values. We show that the ranking of rotation values reduces the number of trials needed by a huge factor and the simple geometric constraints avoid the need for computing the reprojection error in most cases. Though the execution time for the current MATLAB implementation is far from real time requirement, our method can be accelerated significantly by exploiting simplicity and parallelizability of the operations we employ. For eliminating the degenerate case in the state of art pose estimation method, we reformulate the rotation representation. We use unit quaternions instead of CGR parameters used by the method.},\n bibtype = {inproceedings},\n author = {Bhat, K. K. Srikrishna and Heikkila, Janne},\n doi = {10.1109/3DV.2014.27},\n booktitle = {2014 2nd International Conference on 3D Vision}\n}

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\n \n\n \n \n \n \n \n Detection of tumor cell spheroids from co-cultures using phase contrast images and machine learning approach.\n \n \n \n\n\n \n Bayramoglu, N.; Kaakinen, M.; Eklund, L.; Åkerfelt, M.; Nees, M.; Kannala, J.; and Heikkilä, J.\n\n\n \n\n\n\n In Proceedings - International Conference on Pattern Recognition, pages 3345-3350, 2014. IEEE\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Detection of tumor cell spheroids from co-cultures using phase contrast images and machine learning approach},\n type = {inproceedings},\n year = {2014},\n pages = {3345-3350},\n publisher = {IEEE},\n id = {02c9586d-6333-3939-a63b-a1466e6e930b},\n created = {2019-09-15T16:34:27.922Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-15T17:27:45.596Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {© 2014 IEEE. Automated image analysis is demanded in cell biology and drug development research. The type of microscopy is one of the considerations in the trade-offs between experimental setup, image acquisition speed, molecular labelling, resolution and quality of images. In many cases, phase contrast imaging gets higher weights in this optimization. And it comes at the price of reduced image quality in imaging 3D cell cultures. For such data, the existing state-of-the-art computer vision methods perform poorly in segmenting specific cell type. Low SNR, clutter and occlusions are basic challenges for blind segmentation approaches. In this study we propose an automated method, based on a learning framework, for detecting particular cell type in cluttered 2D phase contrast images of 3D cell cultures that overcomes those challenges. It depends on local features defined over super pixels. The method learns appearance based features, statistical features, textural features and their combinations. Also, the importance of each feature is measured by employing Random Forest classifier. Experiments show that our approach does not depend on training data and the parameters.},\n bibtype = {inproceedings},\n author = {Bayramoglu, Neslihan and Kaakinen, Mika and Eklund, Lauri and Åkerfelt, Malin and Nees, Matthias and Kannala, Juho and Heikkilä, Janne},\n doi = {10.1109/ICPR.2014.576},\n booktitle = {Proceedings - International Conference on Pattern Recognition}\n}

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\n \n\n \n \n \n \n \n Automatic detection and analysis of cell motility in phase-contrast time-lapse images using a combination of maximally stable extremal regions and Kalman filter approaches.\n \n \n \n\n\n \n Kaakinen, M.; Huttunen, S.; Paavolainen, L.; Marjomäki, V.; Heikkilä, J.; and Eklund, L.\n\n\n \n\n\n\n Journal of Microscopy, 253(1): 65-78. 2014.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Automatic detection and analysis of cell motility in phase-contrast time-lapse images using a combination of maximally stable extremal regions and Kalman filter approaches},\n type = {article},\n year = {2014},\n keywords = {Automatic cell segmentation,Cell migration,Kalman filter,MSER,Phase-contrast microscopy,Tracking},\n pages = {65-78},\n volume = {253},\n id = {9984b0db-af01-35d9-8f43-8368beabc9b5},\n created = {2019-09-15T16:34:27.962Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-15T17:27:45.224Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {JOUR},\n private_publication = {false},\n abstract = {Phase-contrast illumination is simple and most commonly used microscopic method to observe nonstained living cells. Automatic cell segmentation and motion analysis provide tools to analyze single cell motility in large cell populations. However, the challenge is to find a sophisticated method that is sufficiently accurate to generate reliable results, robust to function under the wide range of illumination conditions encountered in phase-contrast microscopy, and also computationally light for efficient analysis of large number of cells and image frames. To develop better automatic tools for analysis of low magnification phase-contrast images in time-lapse cell migration movies, we investigated the performance of cell segmentation method that is based on the intrinsic properties of maximally stable extremal regions (MSER). MSER was found to be reliable and effective in a wide range of experimental conditions. When compared to the commonly used segmentation approaches, MSER required negligible preoptimization steps thus dramatically reducing the computation time. To analyze cell migration characteristics in time-lapse movies, the MSER-based automatic cell detection was accompanied by a Kalman filter multiobject tracker that efficiently tracked individual cells even in confluent cell populations. This allowed quantitative cell motion analysis resulting in accurate measurements of the migration magnitude and direction of individual cells, as well as characteristics of collective migration of cell groups. Our results demonstrate that MSER accompanied by temporal data association is a powerful tool for accurate and reliable analysis of the dynamic behaviour of cells in phase-contrast image sequences. These techniques tolerate varying and nonoptimal imaging conditions and due to their relatively light computational requirements they should help to resolve problems in computationally demanding and often time-consuming large-scale dynamical analysis of cultured cells.},\n bibtype = {article},\n author = {Kaakinen, Mika and Huttunen, Sami and Paavolainen, Lassi and Marjomäki, Varpu and Heikkilä, Janne and Eklund, Lauri},\n doi = {10.1111/jmi.12098},\n journal = {Journal of Microscopy},\n number = {1}\n}

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\n \n\n \n \n \n \n \n Local phase quantization for blur insensitive texture description.\n \n \n \n\n\n \n Heikkilä, J.; Rahtu, E.; and Ojansivu, V.\n\n\n \n\n\n\n Volume 506 . Studies in Computational Intelligence, pages 49-84. Springer, Berlin, Heidelberg, 2014.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inbook{\n type = {inbook},\n year = {2014},\n pages = {49-84},\n volume = {506},\n publisher = {Springer, Berlin, Heidelberg},\n id = {34addc7a-81cf-3a96-86ca-16838568a3a9},\n created = {2019-09-15T16:34:27.969Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-15T17:27:45.376Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CHAP},\n private_publication = {false},\n bibtype = {inbook},\n author = {Heikkilä, Janne and Rahtu, Esa and Ojansivu, Ville},\n doi = {10.1007/978-3-642-39289-4_3},\n chapter = {Local phase quantization for blur insensitive texture description},\n title = {Studies in Computational Intelligence}\n}

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\n \n 2013\n \n \n (11)\n \n \n

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\n \n\n \n \n \n \n \n Stochastic bottom-up fixation prediction and saccade generation.\n \n \n \n\n\n \n Rezazadegan Tavakoli, H.; Rahtu, E.; and Heikkilä, J.\n\n\n \n\n\n\n Image and Vision Computing, 31(9): 686-693. 2013.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Stochastic bottom-up fixation prediction and saccade generation},\n type = {article},\n year = {2013},\n keywords = {Fixation prediction,Saccadic eye movement,Saliency,Scanpaths},\n pages = {686-693},\n volume = {31},\n id = {c15b0caa-3baf-3e68-bea4-c5b75ee91916},\n created = {2019-09-15T16:34:28.004Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-15T17:27:45.562Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {JOUR},\n private_publication = {false},\n abstract = {In this article, a novel technique for fixation prediction and saccade generation will be introduced. The proposed model simulates saccadic eye movement to incorporate the underlying eye movement mechanism into saliency estimation. To this end, a simple salience measure is introduced. Afterwards, we derive a system model for saccade generation and apply it in a stochastic filtering framework. The proposed model will dynamically make a saccade toward the next predicted fixation and produces saliency maps. Evaluation of the proposed model is carried out in terms of saccade generation performance and saliency estimation. Saccade generation evaluation reveals that the proposed model outperforms inhibition of return. Also, experiments signify integration of eye movement mechanism into saliency estimation boosts the results. Finally, comparison with several saliency models shows the proposed model performs aptly. © 2013 Elsevier B.V.},\n bibtype = {article},\n author = {Rezazadegan Tavakoli, Hamed and Rahtu, Esa and Heikkilä, Janne},\n doi = {10.1016/j.imavis.2013.06.006},\n journal = {Image and Vision Computing},\n number = {9}\n}

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\n \n\n \n \n \n \n \n \n Spherical Center-Surround for Video Saliency Detection Using Sparse Sampling.\n \n \n \n \n\n\n \n Rezazadegan Tavakoli, H.; Rahtu, E.; and Heikkilä, J.\n\n\n \n\n\n\n In Advanced Concepts for Intelligent Vision Systems. ACIVS 2013. Lecture Notes in Computer Science, volume 8192 LNCS, pages 695-704, 2013. Springer, Cham\n \n\n\n\n
\n\n\n\n \n \n $\"Spherical$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Spherical Center-Surround for Video Saliency Detection Using Sparse Sampling},\n type = {inproceedings},\n year = {2013},\n pages = {695-704},\n volume = {8192 LNCS},\n websites = {http://link.springer.com/10.1007/978-3-319-02895-8_62},\n publisher = {Springer, Cham},\n id = {bc54bfca-334d-3564-95ea-ccad5a3088b5},\n created = {2019-09-15T16:34:28.009Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-15T17:49:15.891Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {This paper presents a technique for detection of eminent (salient) regions in an image sequence. The method is inspired by the biological studies on human visual attention systems and is grounded on the famous center-surround theory. It hypothesis that an item (center) is dissimilar to its surrounding. A spherical representation is proposed to estimate amount of salience. It enables the method to integrate computation of temporal and spatial contrast features. Efficient computation of the proposed representation is made possible by sparse sampling the surround which result in an efficient spatiotemporal comparison. The method is evaluated against a recent benchmark methods and is shown to outperform all of them. © 2013 Springer-Verlag.},\n bibtype = {inproceedings},\n author = {Rezazadegan Tavakoli, Hamed and Rahtu, Esa and Heikkilä, Janne},\n doi = {10.1007/978-3-319-02895-8_62},\n booktitle = {Advanced Concepts for Intelligent Vision Systems. ACIVS 2013. Lecture Notes in Computer Science}\n}

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\n \n\n \n \n \n \n \n Gesture interaction for wall-sized touchscreen display.\n \n \n \n\n\n \n Zhai, Y.; Heikkilä, J.; Zhao, G.; Ojala, T.; Alatalo, T.; and Huang, X.\n\n\n \n\n\n\n In UbiComp 2013 Adjunct - Adjunct Publication of the 2013 ACM Conference on Ubiquitous Computing, pages 175-178, 2013. ACM\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@inproceedings{\n title = {Gesture interaction for wall-sized touchscreen display},\n type = {inproceedings},\n year = {2013},\n keywords = {Gesture interaction,Large display,Public display,Touchscreen},\n pages = {175-178},\n publisher = {ACM},\n id = {60107d96-7356-3ab0-be3b-de45f2580031},\n created = {2019-09-15T16:34:28.057Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-15T17:27:45.420Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {In order to improve the user experience in a large touchscreen, this research introduces gesture interaction into wall-sized touchscreen. According to the distance between user and display, we create two interaction modes for touch and body gesture respectively. Challenges encountered and prospects for further improvement are also investigated.},\n bibtype = {inproceedings},\n author = {Zhai, Yan and Heikkilä, Janne and Zhao, Guoying and Ojala, Timo and Alatalo, Toni and Huang, Xinyuan},\n doi = {10.1145/2494091.2494148},\n booktitle = {UbiComp 2013 Adjunct - Adjunct Publication of the 2013 ACM Conference on Ubiquitous Computing}\n}

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\n \n\n \n \n \n \n \n A learned joint depth and intensity prior using Markov random fields.\n \n \n \n\n\n \n Herrera, C., D.; Kannala, J.; Sturm, P.; and Heikkilä, J.\n\n\n \n\n\n\n In Proceedings - 2013 International Conference on 3D Vision, 3DV 2013, pages 17-24, 2013. IEEE\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@inproceedings{\n title = {A learned joint depth and intensity prior using Markov random fields},\n type = {inproceedings},\n year = {2013},\n keywords = {Field of experts,Markov Random Field,depth map inpainting,joint prior},\n pages = {17-24},\n publisher = {IEEE},\n id = {a981e5c1-7261-36d9-b902-7c0adcce287d},\n created = {2019-09-15T16:34:28.099Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2020-09-22T09:09:35.459Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n folder_uuids = {67b5fbfc-903a-4f35-8d95-f58fc1430bfd},\n private_publication = {false},\n abstract = {We present a joint prior that takes intensity and depth information into account. The prior is defined using a flexible Field-of-Experts model and is learned from a database of natural images. It is a generative model and has an efficient method for sampling. We use sampling from the model to perform in painting and up sampling of depth maps when intensity information is available. We show that including the intensity information in the prior improves the results obtained from the model. We also compare to another two-channel inpainting approach and show superior results.},\n bibtype = {inproceedings},\n author = {Herrera, C Daniel and Kannala, Juho and Sturm, Peter and Heikkilä, Janne},\n doi = {10.1109/3DV.2013.11},\n booktitle = {Proceedings - 2013 International Conference on 3D Vision, 3DV 2013}\n}

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\n \n\n \n \n \n \n \n Blur invariant translational image registration for N-fold symmetric blurs.\n \n \n \n\n\n \n Pedone, M.; Flusser, J.; and Heikkilä, J.\n\n\n \n\n\n\n IEEE Transactions on Image Processing, 22(9): 3676-3689. 2013.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Blur invariant translational image registration for N-fold symmetric blurs},\n type = {article},\n year = {2013},\n keywords = {Blurred images,Image registration,N-fold rotation symmetry,Phase correlation},\n pages = {3676-3689},\n volume = {22},\n id = {0c0e0bd6-5253-3878-b7f9-8a7f3a9958bb},\n created = {2019-09-15T16:34:28.105Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-15T17:27:45.093Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {JOUR},\n private_publication = {false},\n abstract = {In this paper, we propose a new registration method designed\\nparticularly for registering differently blurred images. Such a task\\ncannot be successfully resolved by traditional approaches. Our method is\\ninspired by traditional phase correlation, which is now applied to\\ncertain blur-invariant descriptors instead of the original images. This\\nmethod works for unknown blurs assuming the blurring PSF exhibits an\\nN-fold rotational symmetry. It does not require any landmarks. We have\\nexperimentally proven its good performance, which is not dependent on\\nthe amount of blur. In this paper, we explicitly address only\\nregistration with respect to translation, but the method can be readily\\ngeneralized to rotation and scaling.},\n bibtype = {article},\n author = {Pedone, Matteo and Flusser, Jan and Heikkilä, Janne},\n doi = {10.1109/TIP.2013.2268972},\n journal = {IEEE Transactions on Image Processing},\n number = {9}\n}

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\n \n\n \n \n \n \n \n Merging overlapping depth maps into a nonredundant point cloud.\n \n \n \n\n\n \n Kyöstilä, T.; Herrera C., D.; Kannala, J.; and Heikkilä, J.\n\n\n \n\n\n\n In Image Analysis. SCIA 2013. Lecture Notes in Computer Science, volume 7944 LNCS, pages 567-578, 2013. Springer, Berlin, Heidelberg\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n\n\n\n

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@inproceedings{\n title = {Merging overlapping depth maps into a nonredundant point cloud},\n type = {inproceedings},\n year = {2013},\n keywords = {point cloud simplification,surface modeling},\n pages = {567-578},\n volume = {7944 LNCS},\n publisher = {Springer, Berlin, Heidelberg},\n id = {06c0972c-527f-3b04-907a-068ff51fbd22},\n created = {2019-09-15T16:34:28.138Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-19T17:36:37.239Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {Combining long sequences of overlapping depth maps without\\nsimplification results in a huge number of redundant points, which slows\\ndown further processing. In this paper, a novel method is presented for\\nincrementally creating a nonredundant point cloud with varying levels of\\ndetail without limiting the captured volume or requiring any parameters\\nfrom the user. Overlapping measurements are used to refine point\\nestimates by reducing their directional variance. The algorithm was\\nevaluated with plane and cube fitting residuals, which were improved\\nconsiderably over redundant point clouds.},\n bibtype = {inproceedings},\n author = {Kyöstilä, Tomi and Herrera C., Daniel and Kannala, Juho and Heikkilä, Janne},\n doi = {10.1007/978-3-642-38886-6_53},\n booktitle = {Image Analysis. SCIA 2013. Lecture Notes in Computer Science}\n}

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\n \n\n \n \n \n \n \n Saliency detection using joint temporal and spatial decorrelation.\n \n \n \n\n\n \n Tavakoli, H., R.; Rahtu, E.; and Heikkilä, J.\n\n\n \n\n\n\n In Image Analysis. SCIA 2013. Lecture Notes in Computer Science, volume 7944 LNCS, pages 707-717, 2013. Springer, Berlin, Heidelberg\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Saliency detection using joint temporal and spatial decorrelation},\n type = {inproceedings},\n year = {2013},\n pages = {707-717},\n volume = {7944 LNCS},\n publisher = {Springer, Berlin, Heidelberg},\n id = {cf010f93-09c2-3fe3-8e91-84f5a51cfa48},\n created = {2019-09-15T16:34:28.143Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-19T17:36:37.690Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {This article presents a scene-driven (i.e. bottom-up) visual saliency detection technique for videos. The proposed method utilizes non-negative matrix factorization (NMF) to replicate neural responses of primary visual cortex neurons in spatial domain. In temporal domain, principal component analysis (PCA) was applied to imitate the effect of stimulus change experience during neural adaptation phenomena. We apply the proposed saliency model to background subtraction problem. The proposed method does not rely on any background model and is purely unsupervised. In experimental results, it will be shown that the proposed method competes well with some of the state-of-the-art background subtraction techniques especially in dynamic scenes. © 2013 Springer-Verlag.},\n bibtype = {inproceedings},\n author = {Tavakoli, Hamed Rezazadegan and Rahtu, Esa and Heikkilä, Janne},\n doi = {10.1007/978-3-642-38886-6_66},\n booktitle = {Image Analysis. SCIA 2013. Lecture Notes in Computer Science}\n}

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\n \n\n \n \n \n \n \n \n Depth Map Inpainting under a Second-Order Smoothness Prior.\n \n \n \n \n\n\n \n Herrera C., D.; Kannala, J.; Ladický, L.; and Heikkilä, J.\n\n\n \n\n\n\n Volume 7944 LNCS . Image Analysis. SCIA 2013. Lecture Notes in Computer Science, pages 555-566. Springer, Berlin, Heidelberg, 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Image$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@inbook{\n type = {inbook},\n year = {2013},\n keywords = {depth map,graph cut,inpainting,second order prior},\n pages = {555-566},\n volume = {7944 LNCS},\n websites = {http://link.springer.com/10.1007/978-3-642-38886-6_52},\n publisher = {Springer, Berlin, Heidelberg},\n id = {260e87a2-6bcf-35dd-9d6b-defe596a66b5},\n created = {2019-09-15T16:34:28.184Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-19T17:36:37.920Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {Many 3D reconstruction methods produce incomplete depth maps. Depth map\\ninpainting can generate visually plausible structures for the missing\\nareas. We present an inpainting method that encourages flat surfaces\\nwithout favouring fronto-parallel planes. Moreover, it uses a color\\nimage to guide the inpainting and align color and depth edges. We\\nimplement the algorithm efficiently through graph-cuts. We compare the\\nperformance of our method with another inpainting approach used for\\nlarge datasets and we show the results using several datasets. The\\ndepths inpainted with our method are visually plausible and of higher\\nquality.},\n bibtype = {inbook},\n author = {Herrera C., Daniel and Kannala, Juho and Ladický, L'ubor and Heikkilä, Janne},\n doi = {10.1007/978-3-642-38886-6_52},\n chapter = {Depth Map Inpainting under a Second-Order Smoothness Prior},\n title = {Image Analysis. SCIA 2013. Lecture Notes in Computer Science}\n}

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\n \n\n \n \n \n \n \n Sparse motion segmentation using propagation of feature labels.\n \n \n \n\n\n \n Sangi, P.; Hannuksela, J.; Heikkilä, J.; and Silvén, O.\n\n\n \n\n\n\n In VISAPP 2013 - Proceedings of the International Conference on Computer Vision Theory and Applications, volume 2, pages 396-401, 2013. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

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@inproceedings{\n title = {Sparse motion segmentation using propagation of feature labels},\n type = {inproceedings},\n year = {2013},\n keywords = {Block matching,Confidence analysis,Motion segmentation},\n pages = {396-401},\n volume = {2},\n id = {297b94b5-d198-3641-b429-701bda57b808},\n created = {2019-09-15T16:34:28.220Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-19T17:36:37.479Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {The paper considers the problem of extracting background and foreground motions from image sequences based on the estimated displacements of a small set of image blocks. As a novelty, the uncertainty of local motion estimates is analyzed and exploited in the fitting of parametric object motion models which is done within a competitive framework. Prediction of patch labels is based on the temporal propagation of labeling information from seed points in spatial proximity. Estimates of local displacements are then used to predict the object motions which provide a starting point for iterative refinement. Experiments with both synthesized and real image sequences show the potential of the approach as a tool for tracking based online motion segmentation.},\n bibtype = {inproceedings},\n author = {Sangi, Pekka and Hannuksela, Jari and Heikkilä, Janne and Silvén, Olli},\n booktitle = {VISAPP 2013 - Proceedings of the International Conference on Computer Vision Theory and Applications}\n}

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\n \n\n \n \n \n \n \n Local similarity number and its application to object tracking.\n \n \n \n\n\n \n Tavakoli, H., R.; Moin, M., S.; and Heikkilä, J.\n\n\n \n\n\n\n International Journal of Advanced Robotic Systems, 10(3): 184. 2013.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Local similarity number and its application to object tracking},\n type = {article},\n year = {2013},\n keywords = {Local binary patterns,Mean-shift tracking,Saliency},\n pages = {184},\n volume = {10},\n id = {b83966c2-5000-31b3-a538-22c18d41744b},\n created = {2019-09-15T16:34:28.227Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-19T17:36:37.585Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {JOUR},\n private_publication = {false},\n abstract = {In this paper, we present a tracking technique utilizing a simple saliency visual descriptor. Initially, we define a visual descriptor named local similarity pattern that mimics the famous texture operator local binary patterns. The key difference is that it assigns each pixel a code based on the similarity to the neighbouring pixels. Later, we simplify this descriptor to a local saliency operator which counts the number of similar pixels in a neighbourhood. We name this operator local similarity number (LSN). We apply the local similarity number operator to measure the amount of saliency in a target patch and model the target. The proposed tracking algorithm uses a joint saliency-colour histogram to represent the target in a mean-shift tracking framework. We will show that the proposed saliency-colour target representation outperforms texture-colour where texture modelled by local binary patterns and colour target representation techniques are used. © 2013 Tavakoli et al.; licensee InTech.},\n bibtype = {article},\n author = {Tavakoli, Hamed Rezazadegan and Moin, M Shahram and Heikkilä, Janne},\n doi = {10.5772/55337},\n journal = {International Journal of Advanced Robotic Systems},\n number = {3}\n}

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\n \n\n \n \n \n \n \n Temporal saliency for fast motion detection.\n \n \n \n\n\n \n Rezazadegan Tavakoli, H.; Rahtu, E.; and Heikkilä, J.\n\n\n \n\n\n\n In Computer Vision - ACCV 2012 Workshops. ACCV 2012. Lecture Notes in Computer Science, volume 7728 LNCS, pages 321-326, 2013. Springer, Berlin, Heidelberg\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Temporal saliency for fast motion detection},\n type = {inproceedings},\n year = {2013},\n pages = {321-326},\n volume = {7728 LNCS},\n issue = {PART 1},\n publisher = {Springer, Berlin, Heidelberg},\n id = {23cb2a91-1a66-30be-ad3c-d749cac04ab3},\n created = {2019-09-15T16:34:28.301Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-19T17:36:37.284Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {This paper presents a novel saliency detection method and apply it to motion detection. Detection of salient regions in videos or images can reduce the computation power which is needed for complicated tasks such as object recognition. It can also help us to preserve important information in tasks like video compression. Recent advances have given birth to biologically motivated approaches for saliency detection. We perform salience estimation by measuring the change in pixel's intensity value within a temporal interval while performing a filtering step via principal component analysis that is intended to suppress noise. We applied the method to Background Models Challenge (BMC) video data set. Experiments show that the proposed method is apt and accurate. Additionally, the method is fast to compute. © 2013 Springer-Verlag.},\n bibtype = {inproceedings},\n author = {Rezazadegan Tavakoli, Hamed and Rahtu, Esa and Heikkilä, Janne},\n doi = {10.1007/978-3-642-37410-4_29},\n booktitle = {Computer Vision - ACCV 2012 Workshops. ACCV 2012. Lecture Notes in Computer Science}\n}

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\n \n 2012\n \n \n (5)\n \n \n

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\n \n\n \n \n \n \n \n Robust and accurate multi-view reconstruction by prioritized matching.\n \n \n \n\n\n \n Ylimaki, M.; Kannala, J.; Holappa, J.; Heikkilä, J.; and Brandt, S., S.\n\n\n \n\n\n\n In Proceedings - International Conference on Pattern Recognition, pages 2673-2676, 2012. IEEE\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Robust and accurate multi-view reconstruction by prioritized matching},\n type = {inproceedings},\n year = {2012},\n pages = {2673-2676},\n publisher = {IEEE},\n id = {66ac4963-4ff8-3c8c-b7ca-ae1eadc39293},\n created = {2019-09-15T16:34:28.314Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-19T17:36:37.697Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {This paper proposes a prioritized matching approach for finding\\ncorresponding points in multiple calibrated images for multi-view stereo\\nreconstruction. The approach takes a sparse set of seed matches between\\npairs of views as input and then propagates the seeds to neighboring\\nregions by using a prioritized matching method which expands the most\\npromising seeds first. The output of the method is a three-dimensional\\npoint cloud. Unlike previous correspondence growing approaches our\\nmethod allows to use the best-first matching principle in the generic\\nmulti-view stereo setting with arbitrary number of input images. Our\\nexperiments show that matching the most promising seeds first provides\\nvery robust point cloud reconstructions efficiently with just a single\\nexpansion step. A comparison to the current state-of-the-art shows that\\nour method produces reconstructions of similar quality but significantly\\nfaster.},\n bibtype = {inproceedings},\n author = {Ylimaki, Markus and Kannala, Juho and Holappa, Jukka and Heikkilä, Janne and Brandt, Sami S},\n booktitle = {Proceedings - International Conference on Pattern Recognition}\n}

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\n \n\n \n \n \n \n \n Local phase quantization descriptors for blur robust and illumination invariant recognition of color textures.\n \n \n \n\n\n \n Pedone, M.; and Heikkilä, J.\n\n\n \n\n\n\n In Proceedings - International Conference on Pattern Recognition, pages 2476-2479, 2012. IEEE\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Local phase quantization descriptors for blur robust and illumination invariant recognition of color textures},\n type = {inproceedings},\n year = {2012},\n pages = {2476-2479},\n publisher = {IEEE},\n id = {70225bbb-ee47-34fe-8b79-b59ce2b29bac},\n created = {2019-09-15T16:34:28.343Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-19T17:47:33.193Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {A novel extension for color images of the local phase quantization (LPQ) local descriptor is presented. The descriptor is obtained by using a multivector representation of color values in order to derive blur-robust features in frequency domain. We tested the proposed descriptor in texture classification problems, and quantified its robustness for several amounts of blur. The experiments show that the proposed descriptor achieves superior accuracy over its grayscale counterpart and other color texture descriptors. Furthermore its illumination-invariance properties guarantee remarkable performances in challenging scenarios of varying illumination, without the need of preprocessing textures with color-constancy algorithms.},\n bibtype = {inproceedings},\n author = {Pedone, Matteo and Heikkilä, Janne},\n booktitle = {Proceedings - International Conference on Pattern Recognition}\n}

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\n \n\n \n \n \n \n \n Combining textural and geometrical descriptors for scene recognition.\n \n \n \n\n\n \n Bayramog̃lu, N.; Heikkilä, J.; and Pietikäinen, M.\n\n\n \n\n\n\n In Computer Vision – ECCV 2012. Workshops and Demonstrations. ECCV 2012. Lecture Notes in Computer Science, volume 7584 LNCS, pages 32-41, 2012. Springer, Berlin, Heidelberg\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

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@inproceedings{\n title = {Combining textural and geometrical descriptors for scene recognition},\n type = {inproceedings},\n year = {2012},\n keywords = {2D/3D description,feature fusion,localization},\n pages = {32-41},\n volume = {7584 LNCS},\n issue = {PART 2},\n publisher = {Springer, Berlin, Heidelberg},\n id = {61f5f265-cd80-32d7-9086-528150d66416},\n created = {2019-09-15T16:34:28.363Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-19T17:36:38.031Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {Local description of images is a common technique in many computer vision related research. Due to recent improvements in RGB-D cameras, local description of 3D data also becomes practical. The number of studies that make use of this extra information is increasing. However, their applicabilities are limited due to the need for generic combination methods. In this paper, we propose combining textural and geometrical descriptors for scene recognition of RGB-D data. The methods together with the normalization stages proposed in this paper can be applied to combine any descriptors obtained from 2D and 3D domains. This study represents and evaluates different ways of combining multi-modal descriptors within the BoW approach in the context of indoor scene localization. Query's rough location is determined from the pre-recorded images and depth maps in an unsupervised image matching manner.},\n bibtype = {inproceedings},\n author = {Bayramog̃lu, Neslihan and Heikkilä, Janne and Pietikäinen, Matti},\n doi = {10.1007/978-3-642-33868-7_4},\n booktitle = {Computer Vision – ECCV 2012. Workshops and Demonstrations. ECCV 2012. Lecture Notes in Computer Science}\n}

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\n \n\n \n \n \n \n \n Joint depth and color camera calibration with distortion correction.\n \n \n \n\n\n \n Herrera C, D.; Kannala, J.; and Heikkilä, J.\n\n\n \n\n\n\n IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(10): 2058-2064. 2012.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Joint depth and color camera calibration with distortion correction},\n type = {article},\n year = {2012},\n keywords = {Camera calibration,Kinect,camera pair,depth camera,distortion},\n pages = {2058-2064},\n volume = {34},\n id = {a0d8914f-7f34-37fb-9e42-0e5c0d6c4868},\n created = {2019-09-15T16:34:28.403Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2021-04-25T11:28:56.777Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {JOUR},\n folder_uuids = {67b5fbfc-903a-4f35-8d95-f58fc1430bfd,b37b847e-2698-4bae-ad96-1473e506c76b},\n private_publication = {false},\n abstract = {We present an algorithm that simultaneously calibrates two color cameras, a depth camera, and the relative pose between them. The method is designed to have three key features: accurate, practical, and applicable to a wide range of sensors. The method requires only a planar surface to be imaged from various poses. The calibration does not use depth discontinuities in the depth image, which makes it flexible and robust to noise. We apply this calibration to a Kinect device and present a new depth distortion model for the depth sensor. We perform experiments that show an improved accuracy with respect to the manufacturer's calibration.},\n bibtype = {article},\n author = {Herrera C, Daniel and Kannala, Juho and Heikkilä, Janne},\n doi = {10.1109/TPAMI.2012.125},\n journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},\n number = {10}\n}

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\n \n\n \n \n \n \n \n Local phase quantization for blur-insensitive image analysis.\n \n \n \n\n\n \n Rahtu, E.; Heikkilä, J.; Ojansivu, V.; and Ahonen, T.\n\n\n \n\n\n\n Image and Vision Computing, 30(8): 501-512. 2012.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Local phase quantization for blur-insensitive image analysis},\n type = {article},\n year = {2012},\n keywords = {Blur invariance,Face recognition,Feature extraction,Invariant features,Texture recognition},\n pages = {501-512},\n volume = {30},\n id = {06a0eacd-3c2a-32d7-adba-10efd6632257},\n created = {2019-09-15T16:34:28.457Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-19T17:36:37.868Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {JOUR},\n private_publication = {false},\n abstract = {One of the principal causes for image quality degradation is blur. This frequent phenomenon is usually a result of misfocused optics or camera motion, and it is very difficult to undo. Beyond the impaired visual quality, blurring causes problems to computer vision algorithms. In this paper, we present a simple yet powerful image descriptor, which is robust against the most common image blurs. The proposed method is based on quantizing the phase information of the local Fourier transform and it can be used to characterize the underlying image texture. We show how to construct several variants of our descriptor by varying the technique for local phase estimation and utilizing the proposed data decorrelation scheme. The descriptors are assessed in texture and face recognition experiments, and the results are compared with several state-of-the-art methods. The difference to the baseline is considerable in the case of blurred images, but also with sharp images our method gives a highly competitive performance. © 2012 Elsevier B.V. All rights reserved.},\n bibtype = {article},\n author = {Rahtu, Esa and Heikkilä, Janne and Ojansivu, Ville and Ahonen, Timo},\n doi = {10.1016/j.imavis.2012.04.001},\n journal = {Image and Vision Computing},\n number = {8}\n}

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\n \n 2011\n \n \n (9)\n \n \n

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\n \n\n \n \n \n \n \n Camera-based motion recognition for mobile interaction.\n \n \n \n\n\n \n Hannuksela, J.; Barnard, M.; Sangi, P.; and Heikkilä, J.\n\n\n \n\n\n\n ISRN Signal Processing, 2011(1). 2011.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{\n title = {Camera-based motion recognition for mobile interaction},\n type = {article},\n year = {2011},\n volume = {2011},\n id = {c0b03571-ff58-3ded-a40b-0228efcff362},\n created = {2019-09-15T16:34:26.297Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-19T17:36:37.173Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {JOUR},\n private_publication = {false},\n abstract = {Multiple built-in cameras and the small size of mobile phones are underexploited assets for creating novel applications that are ideal for pocket size devices, but may not make much sense with laptops. In this paper we present two vision-based methods for the control of mobile user interfaces based on motion tracking and recognition. In the first case the motion is extracted by estimating the movement of the device held in the user's hand. In the second it is produced from tracking the motion of the user's finger in front of the device. In both alternatives sequences of motion are classified using Hidden Markov Models. The results of the classification are filtered using a likelihood ratio and the velocity entropy to reject possibly incorrect sequences. Our hypothesis here is that incorrect measurements are characterised by a higher entropy value for their velocity histogram denoting more random movements by the user. We also show that using the same filtering criteria we can control unsupervised Maximum A Posteriori adaptation. Experiments conducted on a recognition task involving simple control gestures for mobile phones clearly demonstrate the potential usage of our approaches and may provide for ingredients for new user interface designs.},\n bibtype = {article},\n author = {Hannuksela, Jari and Barnard, Mark and Sangi, Pekka and Heikkilä, Janne},\n doi = {10.5402/2011/425621},\n journal = {ISRN Signal Processing},\n number = {1}\n}

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\n \n\n \n \n \n \n \n Real-time detection of landscape scenes.\n \n \n \n\n\n \n Huttunen, S.; Rahtu, E.; Kunttu, I.; Gren, J.; and Heikkilä, J.\n\n\n \n\n\n\n In Image Analysis. SCIA 2011. Lecture Notes in Computer Science, volume 6688 LNCS, pages 338-347, 2011. Springer, Berlin, Heidelberg\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

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@inproceedings{\n title = {Real-time detection of landscape scenes},\n type = {inproceedings},\n year = {2011},\n keywords = {computational imaging,image categorization,scene classification},\n pages = {338-347},\n volume = {6688 LNCS},\n publisher = {Springer, Berlin, Heidelberg},\n id = {415301c1-38a0-3867-bc58-86cff40614a2},\n created = {2019-09-15T16:34:26.389Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-19T17:36:38.286Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {In this paper we study different approaches that can be used in recognizing landscape scenes. The primary goal has been to find an accurate but still computationally light solution capable of real-time operation. Recognizing landscape images can be thought of a special case of scene classification. Even though there exist a number of different approaches concerning scene classification, there are no other previous works that try to classify images into such high level categories as landscape and non-landscape. This study shows that a global texture-based approach outperforms other more complex methods in the landscape image recognition problem. Furthermore, the results obtained indicate that the computational cost of the method relying on Local Binary Pattern representation is low enough for real-time systems. © 2011 Springer-Verlag.},\n bibtype = {inproceedings},\n author = {Huttunen, Sami and Rahtu, Esa and Kunttu, Iivari and Gren, Juuso and Heikkilä, Janne},\n doi = {10.1007/978-3-642-21227-7_32},\n booktitle = {Image Analysis. SCIA 2011. Lecture Notes in Computer Science}\n}

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\n \n\n \n \n \n \n \n Degradation based blind image quality evaluation.\n \n \n \n\n\n \n Ojansivu, V.; Lepistö, L.; Ilmoniemi, M.; and Heikkilä, J.\n\n\n \n\n\n\n In Image Analysis. SCIA 2011. Lecture Notes in Computer Science, volume 6688 LNCS, pages 306-316, 2011. Springer, Berlin, Heidelberg\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@inproceedings{\n title = {Degradation based blind image quality evaluation},\n type = {inproceedings},\n year = {2011},\n keywords = {blur,exposure,image artifacts,no-reference,quality measurement},\n pages = {306-316},\n volume = {6688 LNCS},\n publisher = {Springer, Berlin, Heidelberg},\n id = {97d6fb04-9f41-39e5-9898-e69d6f656adf},\n created = {2019-09-15T16:34:26.437Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-19T17:36:38.042Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {In this paper, we propose a novel framework for blind image quality evaluation. Unlike the common image quality measures evaluating compression or transmission artifacts this approach analyzes the image properties common to non-ideal image acquisition such as blur, under or over exposure, saturation, and lack of meaningful information. In contrast to methods used for adjusting imaging parameters such as focus and gain this approach does not require any reference image. The proposed method uses seven image degradation features that are extracted and fed to a classifier that decides whether the image has good or bad quality. Most of the features are based on simple image statistics, but we also propose a new feature that proved to be reliable in scene invariant detection of strong blur. For the overall two-class image quality grading, we achieved ≈ 90 % accuracy by using the selected features and the classifier. The method was designed to be computationally efficient in order to enable real-time performance in embedded devices.},\n bibtype = {inproceedings},\n author = {Ojansivu, Ville and Lepistö, Leena and Ilmoniemi, Martti and Heikkilä, Janne},\n doi = {10.1007/978-3-642-21227-7_29},\n booktitle = {Image Analysis. SCIA 2011. Lecture Notes in Computer Science}\n}

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\n \n\n \n \n \n \n \n Volume local phase quantization for blur-insensitive dynamic texture classification.\n \n \n \n\n\n \n Päivärinta, J.; Rahtu, E.; and Heikkilä, J.\n\n\n \n\n\n\n In Image Analysis. SCIA 2011. Lecture Notes in Computer Science, volume 6688 LNCS, pages 360-369, 2011. Springer, Berlin, Heidelberg\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@inproceedings{\n title = {Volume local phase quantization for blur-insensitive dynamic texture classification},\n type = {inproceedings},\n year = {2011},\n keywords = {Local Phase Quantization,Short-Term Fourier Transform,blur-insensitivity,dynamic texture,spatio-temporal domain},\n pages = {360-369},\n volume = {6688 LNCS},\n publisher = {Springer, Berlin, Heidelberg},\n id = {a7ac0935-3f6d-3668-9950-d9593b5d500d},\n created = {2019-09-15T16:34:26.494Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-19T17:36:37.861Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {In this paper, we propose a blur-insensitive descriptor for dynamic textures. The Volume Local Phase Quantization (VLPQ) method introduced is based on binary encoding of the phase information of the local Fourier transform at low frequency points and is an extension to the LPQ operator used for spatial texture analysis. The local Fourier transform is computed efficiently using 1-D convolutions for each dimension in a 3-D volume. The data achieved is compressed to a smaller dimension before a scalar quantization procedure. Finally, a histogram of all binary codewords from dynamic texture is formed. The performance of VLPQ was evaluated both in the case of sharp dynamic textures and spatially blurred dynamic textures. Experiments on a dynamic texture database DynTex++ show that the new method tolerates more spatial blurring than LBP-TOP, which is a state-of-the-art descriptor, and its variant LPQ-TOP.},\n bibtype = {inproceedings},\n author = {Päivärinta, Juhani and Rahtu, Esa and Heikkilä, Janne},\n doi = {10.1007/978-3-642-21227-7_34},\n booktitle = {Image Analysis. SCIA 2011. Lecture Notes in Computer Science}\n}

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\n \n\n \n \n \n \n \n Accurate and practical calibration of a depth and color camera pair.\n \n \n \n\n\n \n Herrera C., D.; Kannala, J.; and Heikkilä, J.\n\n\n \n\n\n\n In Computer Analysis of Images and Patterns. CAIP 2011. Lecture Notes in Computer Science, volume 6855 LNCS, pages 437-445, 2011. Springer, Berlin, Heidelberg\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

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@inproceedings{\n title = {Accurate and practical calibration of a depth and color camera pair},\n type = {inproceedings},\n year = {2011},\n keywords = {calibration,camera pair,depth camera},\n pages = {437-445},\n volume = {6855 LNCS},\n issue = {PART 2},\n publisher = {Springer, Berlin, Heidelberg},\n id = {3e8f211d-0ee5-3d8f-b267-12303c7f60d3},\n created = {2019-09-15T16:34:26.530Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-19T17:36:37.482Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {We present an algorithm that simultaneously calibrates a color camera, a depth camera, and the relative pose between them. The method is designed to have three key features that no other available algorithm currently has: accurate, practical, applicable to a wide range of sensors. The method requires only a planar surface to be imaged from various poses. The calibration does not use color or depth discontinuities in the depth image which makes it flexible and robust to noise. We perform experiments with particular depth sensor and achieve the same accuracy as the propietary calibration procedure of the manufacturer.},\n bibtype = {inproceedings},\n author = {Herrera C., Daniel and Kannala, Juho and Heikkilä, Janne},\n doi = {10.1007/978-3-642-23678-5_52},\n booktitle = {Computer Analysis of Images and Patterns. CAIP 2011. Lecture Notes in Computer Science}\n}

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\n \n\n \n \n \n \n \n Multi-view alpha matte for free viewpoint rendering.\n \n \n \n\n\n \n Herrera C., D.; Kannala, J.; and Heikkilä, J.\n\n\n \n\n\n\n In Computer Vision/Computer Graphics Collaboration Techniques. MIRAGE 2011. Lecture Notes in Computer Science, volume 6930 LNCS, pages 98-109, 2011. Springer, Berlin, Heidelberg\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Multi-view alpha matte for free viewpoint rendering},\n type = {inproceedings},\n year = {2011},\n pages = {98-109},\n volume = {6930 LNCS},\n publisher = {Springer, Berlin, Heidelberg},\n id = {2b31b40a-a6b4-3557-a98f-4ae8b19f3af6},\n created = {2019-09-15T16:34:26.608Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-19T17:36:38.282Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Herrera C., Daniel and Kannala, Juho and Heikkilä, Janne},\n doi = {10.1007/978-3-642-24136-9_9},\n booktitle = {Computer Vision/Computer Graphics Collaboration Techniques. MIRAGE 2011. Lecture Notes in Computer Science}\n}

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\n \n\n \n \n \n \n \n Robust airlight estimation for haze removal from a single image.\n \n \n \n\n\n \n Pedone, M.; and Heikkilä, J.\n\n\n \n\n\n\n In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pages 90-96, 2011. IEEE\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Robust airlight estimation for haze removal from a single image},\n type = {inproceedings},\n year = {2011},\n pages = {90-96},\n publisher = {IEEE},\n id = {804571e4-8489-3c5b-9b2c-211791bedd24},\n created = {2019-09-15T16:34:26.693Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-19T17:36:37.874Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {Present methods for haze removal from a single image require the estimation of two physical quantities which, according to the commonly used atmospheric scattering model, are transmission and airlight. The visual quality of images de-hazed with such methods is highly dependent on the accuracy ofestimation ofthe aforementioned quantities. In this paper we propose a new method for reliable airlight color estimation that could be used in digital cameras to au- tomatically de-haze images by removing unrealistic color artifacts. The main idea of our method is based on novel statistics gathered from natural images regarding frequently occurring airlight colors. The statistics are used to intro- duce a minimization cost functional which has a closed form solution, and is easy to compute. We compare our approach with current methods present in literature, and show its su- perior robustness with both images with artificially added haze, and real hazy photos.},\n bibtype = {inproceedings},\n author = {Pedone, Matteo and Heikkilä, Janne},\n doi = {10.1109/CVPRW.2011.5981822},\n booktitle = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops}\n}

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\n \n\n \n \n \n \n \n Fast and efficient saliency detection using sparse sampling and kernel density estimation.\n \n \n \n\n\n \n Rezazadegan Tavakoli, H.; Rahtu, E.; and Heikkilä, J.\n\n\n \n\n\n\n In Image Analysis. SCIA 2011. Lecture Notes in Computer Science, volume 6688 LNCS, pages 666-675, 2011. Springer, Berlin, Heidelberg\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

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@inproceedings{\n title = {Fast and efficient saliency detection using sparse sampling and kernel density estimation},\n type = {inproceedings},\n year = {2011},\n keywords = {Saliency detection,discriminant center-surround,eye-fixation},\n pages = {666-675},\n volume = {6688 LNCS},\n publisher = {Springer, Berlin, Heidelberg},\n id = {2ea308ff-b536-322a-b258-e78400381c95},\n created = {2019-09-15T16:34:26.737Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-19T17:36:37.951Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {Salient region detection has gained a great deal of attention in computer vision. It is useful for applications such as adaptive video/image compression, image segmentation, anomaly detection, image retrieval, etc. In this paper, we study saliency detection using a center-surround approach. The proposed method is based on estimating saliency of local feature contrast in a Bayesian framework. The distributions needed are estimated particularly using sparse sampling and kernel density estimation. Furthermore, the nature of method implicitly considers what refereed to as center bias in literature. Proposed method was evaluated on a publicly available data set which contains human eye fixation as ground-truth. The results indicate more than 5% improvement over state-of-the-art methods. Moreover, the method is fast enough to run in real-time. © 2011 Springer-Verlag.},\n bibtype = {inproceedings},\n author = {Rezazadegan Tavakoli, Hamed and Rahtu, Esa and Heikkilä, Janne},\n doi = {10.1007/978-3-642-21227-7_62},\n booktitle = {Image Analysis. SCIA 2011. Lecture Notes in Computer Science}\n}

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\n \n\n \n \n \n \n \n Generating dense depth maps using a patch cloud and local planar surface models.\n \n \n \n\n\n \n Daniel, H., C.; Kannala, J.; and Heikkilä, J.\n\n\n \n\n\n\n In 3DTV Conference: The True Vision - Capture, Transmission and Display of 3D Video, 3DTV-CON 2011 - Proceedings, pages 1-4, 2011. IEEE\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n\n\n\n

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@inproceedings{\n title = {Generating dense depth maps using a patch cloud and local planar surface models},\n type = {inproceedings},\n year = {2011},\n keywords = {Computer vision,Stereo image processing},\n pages = {1-4},\n publisher = {IEEE},\n id = {bbdc634d-9891-360e-a524-9c3b4341f6d6},\n created = {2019-09-15T16:34:28.662Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-19T17:36:37.669Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {Patch cloud based multi-view stereo methods have proven to be an accurate and scalable approach for scene reconstruction. Their applicability, however, is limited due to the semi-dense nature of their reconstruction. We propose a method to generate a dense depth map from a patch cloud by assuming a planar surface model for non-reconstructed areas. We use local evidence to estimate the best fitting plane around missing areas. We then apply a graph cut optimization to select the best plane for each pixel. We demonstrate our approach with a challenging scene containing planar and non-planar surfaces.},\n bibtype = {inproceedings},\n author = {Daniel, Herrera C. and Kannala, Juho and Heikkilä, Janne},\n doi = {10.1109/3DTV.2011.5877169},\n booktitle = {3DTV Conference: The True Vision - Capture, Transmission and Display of 3D Video, 3DTV-CON 2011 - Proceedings}\n}

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\n \n 2010\n \n \n (9)\n \n \n

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\n \n\n \n \n \n \n \n The structural form in image categorization.\n \n \n \n\n\n \n Hanni, J.; Rahtu, E.; and Heikkilä, J.\n\n\n \n\n\n\n In VISAPP 2010 - Proceedings of the International Conference on Computer Vision Theory and Applications, volume 2, pages 345-350, 2010. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

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@inproceedings{\n title = {The structural form in image categorization},\n type = {inproceedings},\n year = {2010},\n keywords = {Clustering,Generative model,Image categorization},\n pages = {345-350},\n volume = {2},\n id = {4a66cb89-cf3b-351e-8865-fa116c98c240},\n created = {2019-09-15T16:34:26.455Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-19T17:42:20.049Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Hanni, Juha and Rahtu, Esa and Heikkilä, Janne},\n booktitle = {VISAPP 2010 - Proceedings of the International Conference on Computer Vision Theory and Applications}\n}

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\n \n\n \n \n \n \n \n Multi-object tracking based on soft assignment of detection responses.\n \n \n \n\n\n \n Huttunen, S.; and Heikkilä, J.\n\n\n \n\n\n\n In VISAPP 2010 - Proceedings of the International Conference on Computer Vision Theory and Applications, volume 1, pages 296-301, 2010. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

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@inproceedings{\n title = {Multi-object tracking based on soft assignment of detection responses},\n type = {inproceedings},\n year = {2010},\n keywords = {Data association,Kalman filter,Multi-object tracking},\n pages = {296-301},\n volume = {1},\n id = {abee405d-dbc0-3a34-8a4a-a8948399d88a},\n created = {2019-09-15T16:34:26.734Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-19T17:42:20.106Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Huttunen, Sami and Heikkilä, Janne},\n booktitle = {VISAPP 2010 - Proceedings of the International Conference on Computer Vision Theory and Applications}\n}

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\n \n\n \n \n \n \n \n \n Segmenting Salient Objects from Images and Videos.\n \n \n \n \n\n\n \n Rahtu, E.; Kannala, J.; Salo, M.; and Heikkilä, J.\n\n\n \n\n\n\n In Computer Vision – ECCV 2010. ECCV 2010. Lecture Notes in Computer Science, volume 6315 LNCS, pages 366-379, 2010. Springer, Berlin, Heidelberg\n \n\n\n\n
\n\n\n\n \n \n $\"Segmenting$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

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@inproceedings{\n title = {Segmenting Salient Objects from Images and Videos},\n type = {inproceedings},\n year = {2010},\n keywords = {Saliency measure,background subtraction,segmentation},\n pages = {366-379},\n volume = {6315 LNCS},\n issue = {PART 5},\n websites = {http://link.springer.com/10.1007/978-3-642-15555-0_27},\n publisher = {Springer, Berlin, Heidelberg},\n id = {b8f0311f-8c2c-3bf0-a92f-8510c371bf0f},\n created = {2019-09-15T16:34:27.416Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-16T06:40:23.719Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {In this paper we introduce a new salient object segmentation method, which is based on combining a saliency measure with a conditional random field (CRF) model. The proposed saliency measure is formulated using a statistical framework and local feature contrast in illumination, color, and motion information. The resulting saliency map is then used in a CRF model to define an energy minimization based segmentation approach, which aims to recover well-defined salient objects. The method is efficiently implemented by using the integral histogram approach and graph cut solvers. Compared to previous approaches the introduced method is among the few which are applicable to both still images and videos including motion cues. The experiments show that our approach outperforms the current state-of-the-art methods in both qualitative and quantitative terms.},\n bibtype = {inproceedings},\n author = {Rahtu, Esa and Kannala, Juho and Salo, Mikko and Heikkilä, Janne},\n doi = {10.1007/978-3-642-15555-0_27},\n booktitle = {Computer Vision – ECCV 2010. ECCV 2010. Lecture Notes in Computer Science}\n}

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\n \n\n \n \n \n \n \n Evaluation of denoising methods with RAW images and perceptual measures.\n \n \n \n\n\n \n Pedone, M.; Heikkilä, J.; Nikkanen, J.; Lepistö, L.; and Kaikumaa, T.\n\n\n \n\n\n\n In VISAPP 2010 - Proceedings of the International Conference on Computer Vision Theory and Applications, volume 1, pages 168-173, 2010. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@inproceedings{\n title = {Evaluation of denoising methods with RAW images and perceptual measures},\n type = {inproceedings},\n year = {2010},\n keywords = {Artifacts,Degradation,Demosaic,Denoise,Evaluation,Perceptual quality assessment,RAW images,Real data,State-of-the-art},\n pages = {168-173},\n volume = {1},\n id = {1828f2f9-726d-38bb-b3ba-937caecb245b},\n created = {2019-09-15T16:34:28.062Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-15T17:27:45.298Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Pedone, Matteo and Heikkilä, Janne and Nikkanen, Jarno and Lepistö, Leena and Kaikumaa, Timo},\n booktitle = {VISAPP 2010 - Proceedings of the International Conference on Computer Vision Theory and Applications}\n}

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\n \n\n \n \n \n \n \n Isotropic granularity-tunable gradients partition (IGGP) descriptors for human detection.\n \n \n \n\n\n \n Liu, Y.; and Heikkilä, J.\n\n\n \n\n\n\n In British Machine Vision Conference, BMVC 2010 - Proceedings, pages 1-11, 2010. \n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Isotropic granularity-tunable gradients partition (IGGP) descriptors for human detection},\n type = {inproceedings},\n year = {2010},\n pages = {1-11},\n id = {121e192d-83a2-3d36-b704-babb4ac1fb72},\n created = {2019-09-15T16:34:28.458Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-19T17:42:19.997Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Liu, Yazhou and Heikkilä, Janne},\n doi = {10.5244/C.24.63},\n booktitle = {British Machine Vision Conference, BMVC 2010 - Proceedings}\n}

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\n \n\n \n \n \n \n \n Spatial-temporal granularity-tunable gradients partition (STGGP) descriptors for human detection.\n \n \n \n\n\n \n Liu, Y.; Shan, S.; Chen, X.; Heikkilä, J.; Gao, W.; and Pietikainen, M.\n\n\n \n\n\n\n In Computer Vision – ECCV 2010. ECCV 2010. Lecture Notes in Computer Science, volume 6311 LNCS, pages 327-340, 2010. Springer, Berlin, Heidelberg\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Spatial-temporal granularity-tunable gradients partition (STGGP) descriptors for human detection},\n type = {inproceedings},\n year = {2010},\n pages = {327-340},\n volume = {6311 LNCS},\n issue = {PART 1},\n publisher = {Springer, Berlin, Heidelberg},\n id = {7b384a4c-1b8a-318e-a4b4-0838ae8a7d73},\n created = {2019-09-15T16:34:28.506Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-19T17:42:20.104Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {Computer Vision – ECCV 2010},\n bibtype = {inproceedings},\n author = {Liu, Yazhou and Shan, Shiguang and Chen, Xilin and Heikkilä, Janne and Gao, Wen and Pietikainen, Matti},\n doi = {10.1007/978-3-642-15549-9_24},\n booktitle = {Computer Vision – ECCV 2010. ECCV 2010. Lecture Notes in Computer Science}\n}

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\n \n\n \n \n \n \n \n Improved blur insensitivity for decorrelated local phase quantization.\n \n \n \n\n\n \n Heikkilä, J.; Ojansivu, V.; and Rahtu, E.\n\n\n \n\n\n\n In Proceedings - International Conference on Pattern Recognition, pages 818-821, 2010. IEEE\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Improved blur insensitivity for decorrelated local phase quantization},\n type = {inproceedings},\n year = {2010},\n pages = {818-821},\n publisher = {IEEE},\n id = {3de9a404-ec0a-3873-bc60-37fd0e6fdf1d},\n created = {2019-09-15T16:34:28.548Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-19T17:36:37.334Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {This paper presents a novel blur tolerant decor relation scheme for local phase quantization (LPQ) texture descriptor. As opposed to previous methods, the introduced model can be applied with virtually any kind of blur regardless of the point spread function. The new technique takes also into account the changes in the image characteristics originating from the blur itself. The implementation does not suffer from multiple solutions like the decor relation in original LPQ, but still retains the same run-time computational complexity. The texture classification experiments illustrate considerable improvements in the performance of LPQ descriptors in the case of blurred images and show only negligible loss of accuracy with sharp images.},\n bibtype = {inproceedings},\n author = {Heikkilä, Janne and Ojansivu, Ville and Rahtu, Esa},\n doi = {10.1109/ICPR.2010.206},\n booktitle = {Proceedings - International Conference on Pattern Recognition}\n}

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\n \n\n \n \n \n \n \n Compressing sparse feature vectors using random ortho-projections.\n \n \n \n\n\n \n Rahtu, E.; Salo, M.; and Heikkilä, J.\n\n\n \n\n\n\n In Proceedings - International Conference on Pattern Recognition, pages 1397-1400, 2010. IEEE\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Compressing sparse feature vectors using random ortho-projections},\n type = {inproceedings},\n year = {2010},\n pages = {1397-1400},\n publisher = {IEEE},\n id = {c64550b0-c04f-3fb5-a4f3-65f91a5851d5},\n created = {2019-09-15T16:34:28.579Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-19T17:36:38.217Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {In this paper we investigate the usage of random ortho-projections in the compression of sparse feature vectors. The study is carried out by evaluating the compressed features in classification tasks instead of concentrating on reconstruction accuracy. In the random ortho-projection method, the mapping for the compression can be obtained without any further knowledge of the original features. This makes the approach favorable if training data is costly or impossible to obtain. The independence from the data also enables one to embed the compression scheme directly into the computation of the original features. Our study is inspired by the results in compressive sensing, which state that up to a certain compression ratio and with high probability, such projections result in no loss of information. In comparison to learning based compression, namely principal component analysis (PCA), the random projections resulted in comparable performance already at high compression ratios depending on the sparsity of the original features.},\n bibtype = {inproceedings},\n author = {Rahtu, Esa and Salo, Mikko and Heikkilä, Janne},\n doi = {10.1109/ICPR.2010.345},\n booktitle = {Proceedings - International Conference on Pattern Recognition}\n}

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\n \n\n \n \n \n \n \n A human detection framework for heavy machinery.\n \n \n \n\n\n \n Heimonen, T.; and Heikkilä, J.\n\n\n \n\n\n\n In Proceedings - International Conference on Pattern Recognition, pages 416-419, 2010. IEEE\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {A human detection framework for heavy machinery},\n type = {inproceedings},\n year = {2010},\n pages = {416-419},\n publisher = {IEEE},\n id = {ba45538f-517a-35de-8886-400f5311adf2},\n created = {2019-09-15T16:34:28.650Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-19T17:36:37.519Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {A stereo camera based human detection framework for heavy machinery is proposed. The framework allows easy integration of different human detection and image segmentation methods. This integration is essential for diverge and challenging work machine environments, in which traditional, one detector based human detection approaches has been found to be insufficient. The framework is based on the idea of pixel-wise human probabilities, which are obtained by several separate detection trials following binomial distribution. The framework has been evaluated with extensive image sequences of authentic work machine environments, and it has proven to be feasible. Promising detection performance was achieved by utilizing publically available human detectors.},\n bibtype = {inproceedings},\n author = {Heimonen, Teuvo and Heikkilä, Janne},\n doi = {10.1109/ICPR.2010.110},\n booktitle = {Proceedings - International Conference on Pattern Recognition}\n}

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\n \n 2009\n \n \n (7)\n \n \n

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\n \n\n \n \n \n \n \n Weighted DFT based blur invariants for pattern recognition.\n \n \n \n\n\n \n Ojansivu, V.; and Heikkilä, J.\n\n\n \n\n\n\n In Image Analysis. SCIA 2009. Lecture Notes in Computer Science, volume 5575 LNCS, pages 71-80, 2009. Springer, Berlin, Heidelberg\n \n\n\n\n
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@inproceedings{\n title = {Weighted DFT based blur invariants for pattern recognition},\n type = {inproceedings},\n year = {2009},\n pages = {71-80},\n volume = {5575 LNCS},\n publisher = {Springer, Berlin, Heidelberg},\n id = {a37ff5e8-669c-390b-8985-cc250781bc15},\n created = {2019-09-15T16:34:26.696Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-23T18:20:08.144Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Ojansivu, Ville and Heikkilä, Janne},\n doi = {10.1007/978-3-642-02230-2_8},\n booktitle = {Image Analysis. SCIA 2009. Lecture Notes in Computer Science}\n}

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\n \n\n \n \n \n \n \n Dense and deformable motion segmentation for wide baseline images.\n \n \n \n\n\n \n Kannala, J.; Rahtu, E.; Brandt, S., S.; and Heikkilä, J.\n\n\n \n\n\n\n In Image Analysis. SCIA 2009. Lecture Notes in Computer Science, volume 5575 LNCS, pages 379-389, 2009. Springer, Berlin, Heidelberg\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Dense and deformable motion segmentation for wide baseline images},\n type = {inproceedings},\n year = {2009},\n pages = {379-389},\n volume = {5575 LNCS},\n publisher = {Springer, Berlin, Heidelberg},\n id = {9d16c403-9710-3c1e-9c75-e5272950352b},\n created = {2019-09-15T16:34:26.845Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-23T18:20:08.816Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Kannala, Juho and Rahtu, Esa and Brandt, Sami S and Heikkilä, Janne},\n doi = {10.1007/978-3-642-02230-2_39},\n booktitle = {Image Analysis. SCIA 2009. Lecture Notes in Computer Science}\n}

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\n \n\n \n \n \n \n \n Applying visual object categorization and memory colors for automatic color constancy.\n \n \n \n\n\n \n Rahtu, E.; Nikkanen, J.; Kannala, J.; Lepistö, L.; and Heikkilä, J.\n\n\n \n\n\n\n In Image Analysis and Processing – ICIAP 2009. ICIAP 2009. Lecture Notes in Computer Science, volume 5716 LNCS, pages 873-882, 2009. Springer, Berlin, Heidelberg\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@inproceedings{\n title = {Applying visual object categorization and memory colors for automatic color constancy},\n type = {inproceedings},\n year = {2009},\n keywords = {Category segmentation,Color constancy,Memory color,Object categorization,Raw image},\n pages = {873-882},\n volume = {5716 LNCS},\n publisher = {Springer, Berlin, Heidelberg},\n id = {deea36db-9a1d-3f8e-b3cc-cd0e2e95dcfe},\n created = {2019-09-15T16:34:26.971Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-23T18:20:08.146Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {This paper presents a framework for using high-level visual information to enhance the performance of automatic color constancy algorithms. The approach is based on recognizing special visual object categories, called here as memory color categories, which have a relatively constant color (e.g. the sky). If such category is found from image, the initial white balance provided by a low-level color constancy algorithm can be adjusted so that the observed color of the category moves toward the desired color. The magnitude and direction of the adjustment is controlled by the learned characteristics of the particular category in the chromaticity space. The object categorization is performed using bag-of-features method and raw camera data with reduced preprocessing and resolution. The proposed approach is demonstrated in experiments involving the standard gray-world and the state-of-the-art gray-edge color constancy methods. In both cases the introduced approach improves the performance of the original methods.},\n bibtype = {inproceedings},\n author = {Rahtu, Esa and Nikkanen, Jarno and Kannala, Juho and Lepistö, Leena and Heikkilä, Janne},\n doi = {10.1007/978-3-642-04146-4_93},\n booktitle = {Image Analysis and Processing – ICIAP 2009. ICIAP 2009. Lecture Notes in Computer Science}\n}

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\n \n\n \n \n \n \n \n Affine invariant features in pattern recognition.\n \n \n \n\n\n \n Rahtu, E.; and Heikkilä, J.\n\n\n \n\n\n\n Handbook of Pattern Recognition and Computer Vision, Fourth Edition, pages 235-255. 2009.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inbook{\n type = {inbook},\n year = {2009},\n pages = {235-255},\n id = {b82dbf26-6fee-396c-b4ec-4af9546d9abb},\n created = {2019-09-15T16:34:28.395Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-19T17:42:19.993Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CHAP},\n private_publication = {false},\n bibtype = {inbook},\n author = {Rahtu, Esa and Heikkilä, Janne},\n doi = {10.1142/9789814273398_010},\n chapter = {Affine invariant features in pattern recognition},\n title = {Handbook of Pattern Recognition and Computer Vision, Fourth Edition}\n}

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\n \n\n \n \n \n \n \n Methods for local phase quantization in blur-insensitive image analysis.\n \n \n \n\n\n \n Heikkila, J.; and Ojansivu, V.\n\n\n \n\n\n\n In 2009 International Workshop on Local and Non-Local Approximation in Image Processing, pages 104-111, 2009. IEEE\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Methods for local phase quantization in blur-insensitive image analysis},\n type = {inproceedings},\n year = {2009},\n pages = {104-111},\n publisher = {IEEE},\n id = {f990a104-6623-37c1-b688-edc0495a452c},\n created = {2019-09-15T16:34:28.541Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-23T18:20:08.282Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Heikkila, Janne and Ojansivu, Ville},\n booktitle = {2009 International Workshop on Local and Non-Local Approximation in Image Processing}\n}

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\n \n\n \n \n \n \n \n A simple and efficient saliency detector for background subtraction.\n \n \n \n\n\n \n Rahtu, E.; and Heikkilä, J.\n\n\n \n\n\n\n In 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009, pages 1137-1144, 2009. IEEE\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {A simple and efficient saliency detector for background subtraction},\n type = {inproceedings},\n year = {2009},\n pages = {1137-1144},\n publisher = {IEEE},\n id = {8a48812a-ab26-3a1d-b5a9-a48be6958ff8},\n created = {2019-09-15T16:34:28.859Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-23T18:20:08.216Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {In this paper we present a simple and computationally efficient method for detecting visually salient areas. The proposed method is based on searching image segments whose intensity values are more accurately described by the intensity distribution of the object compared to the distribution of the surrounding area. The practical implementation applies a sliding window approach, where the distributions of the objects and surroundings are estimated using semi-local intensity histograms that are efficiently evaluated using integral histogram approach. The introduced approach requires no training and no additional segmentation algorithms. Saliency detection can be used in background subtraction and we show that the proposed method is especially effective in the case where the scene is highly dynamic or the camera is not still. Furthermore using our approach we are able to detect also targets that are not moving. Comparisons with state-of-the-art saliency detectors and background subtraction techniques indicate that the introduced approach results in high performance and accuracy, outperforming the reference methods.},\n bibtype = {inproceedings},\n author = {Rahtu, Esa and Heikkilä, Janne},\n doi = {10.1109/ICCVW.2009.5457577},\n booktitle = {2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009}\n}

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\n \n\n \n \n \n \n \n Self-calibration of central cameras from point correspondences by minimizing angular error.\n \n \n \n\n\n \n Kannala, J.; Brandt, S., S.; and Heikkilä, J.\n\n\n \n\n\n\n In Computer Vision and Computer Graphics. Theory and Applications. VISIGRAPP 2008. Communications in Computer and Information Science, volume 24 CCIS, pages 109-122, 2009. Springer, Berlin, Heidelberg\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Self-calibration of central cameras from point correspondences by minimizing angular error},\n type = {inproceedings},\n year = {2009},\n pages = {109-122},\n volume = {24 CCIS},\n publisher = {Springer, Berlin, Heidelberg},\n id = {7ed55845-57df-35c8-a7f5-9e504a8d4f07},\n created = {2019-09-23T18:20:07.555Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-23T18:20:07.555Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Kannala, Juho and Brandt, Sami S and Heikkilä, Janne},\n doi = {10.1007/978-3-642-10226-4_9},\n booktitle = {Computer Vision and Computer Graphics. Theory and Applications. VISIGRAPP 2008. Communications in Computer and Information Science}\n}

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\n \n 2008\n \n \n (18)\n \n \n

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\n \n\n \n \n \n \n \n \n Recognition of blurred faces using Local Phase Quantization.\n \n \n \n \n\n\n \n Ahonen, T.; Rahtu, E.; Ojansivu, V.; and Heikkilä, J.\n\n\n \n\n\n\n In 19th International Conference on Pattern Recognition, pages 1-4, 12 2008. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"Recognition$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Recognition of blurred faces using Local Phase Quantization},\n type = {inproceedings},\n year = {2008},\n pages = {1-4},\n websites = {http://ieeexplore.ieee.org/document/4761847/},\n month = {12},\n publisher = {IEEE},\n id = {eb46db8e-9b54-3987-a023-ed14d643ce62},\n created = {2019-09-15T16:34:26.095Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-23T18:20:07.775Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Ahonen, Timo and Rahtu, Esa and Ojansivu, Ville and Heikkilä, Janne},\n doi = {10.1109/ICPR.2008.4761847},\n booktitle = {19th International Conference on Pattern Recognition}\n}

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\n \n\n \n \n \n \n \n A feature guided particle filter for robust hand tracking.\n \n \n \n\n\n \n Okkonen, M., A.; Heikkilä, J.; and Pietikäinen, M.\n\n\n \n\n\n\n In VISAPP 2008 - 3rd International Conference on Computer Vision Theory and Applications, Proceedings, volume 2, pages 368-374, 2008. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@inproceedings{\n title = {A feature guided particle filter for robust hand tracking},\n type = {inproceedings},\n year = {2008},\n keywords = {Adaptive color model,Hand tracking,Importance sampling,Particle filtering},\n pages = {368-374},\n volume = {2},\n id = {c7588f41-0d35-33b1-8c3c-535f365527c2},\n created = {2019-09-15T16:34:26.652Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-23T18:20:08.640Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Okkonen, Matti Antero and Heikkilä, Janne and Pietikäinen, Matti},\n booktitle = {VISAPP 2008 - 3rd International Conference on Computer Vision Theory and Applications, Proceedings}\n}

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\n \n\n \n \n \n \n \n Geometric Camera Calibration.\n \n \n \n\n\n \n Kannala, J.; Heikkilä, J.; and Brandt, S., S.\n\n\n \n\n\n\n Wiley Encyclopedia of Computer Science and Engineering, pages 1-11. 2008.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inbook{\n type = {inbook},\n year = {2008},\n pages = {1-11},\n id = {c4f58abe-993e-3cbb-9423-2ab8f6222e68},\n created = {2019-09-15T16:34:26.815Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-26T17:24:07.422Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {JOUR},\n private_publication = {false},\n abstract = {Geometric camera calibration is a prerequisite for making accurate geometric measurements from image data, and hence it is a fundamental task in computer vision. This article gives a discussion about the camera models and calibration methods used in the field. The emphasis is on conventional calibration methods in which the parameters of the camera model are determined by using images of a calibration object whose geometric properties are known. The presented techniques are illustrated with real calibration examples in which several different kinds of cameras are calibrated using a planar calibration object.},\n bibtype = {inbook},\n author = {Kannala, Juho and Heikkilä, Janne and Brandt, Sami S},\n doi = {10.1002/9780470050118.ecse589},\n chapter = {Geometric Camera Calibration},\n title = {Wiley Encyclopedia of Computer Science and Engineering}\n}

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\n \n\n \n \n \n \n \n On bin configuration of shape context descriptors in human silhouette classification.\n \n \n \n\n\n \n Barnard, M.; and Heikkilä, J.\n\n\n \n\n\n\n In Advanced Concepts for Intelligent Vision Systems. ACIVS 2008. Lecture Notes in Computer Science, volume 5259 LNCS, pages 850-859, 2008. Springer, Berlin, Heidelberg\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {On bin configuration of shape context descriptors in human silhouette classification},\n type = {inproceedings},\n year = {2008},\n pages = {850-859},\n volume = {5259 LNCS},\n publisher = {Springer, Berlin, Heidelberg},\n id = {ea343992-c534-3da6-842a-9cec77d2e3d2},\n created = {2019-09-15T16:34:26.934Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-23T18:20:08.936Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {Shape context descriptors have been a valuable tool in shape description\\nsince their introduction. In this paper we examine the performance of\\nshape context descriptors in the presence of noisy human silhouette\\ndata. Shape context descriptors have been shown to be robust to Gaussian\\nnoise in the task of shape matching. We implement four different\\nconfigurations of shape context by altering the spacing of the histogram\\nbins and then test the performance of these configurations in the\\npresence of noise. The task used for these tests is recognition of body\\npart shapes in human silhouettes. The noise in human Silhouettes is\\nprincipally from three sources: the noise from errors in silhouette\\nsegmentation, noise from loose clothing and noise from occlusions. We\\nshow that in the presence of this noise a newly proposed spacing for the\\nshape context histogram bins has the best performance.},\n bibtype = {inproceedings},\n author = {Barnard, Mark and Heikkilä, Janne},\n doi = {10.1007/978-3-540-88458-3-77},\n booktitle = {Advanced Concepts for Intelligent Vision Systems. ACIVS 2008. Lecture Notes in Computer Science}\n}

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\n \n\n \n \n \n \n \n Affective pictures and emotion analysis of facial expressions with local binary pattern operator: Preliminary results.\n \n \n \n\n\n \n Laukka, S., J.; Rantanen, A.; Zhao, G.; Taini, M.; and Heikkilä, J.\n\n\n \n\n\n\n In Proceedings of EHTI'08: The First Finnish Symposium on Emotions and Human-Technology Interaction, pages 18, 2008. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Affective pictures and emotion analysis of facial expressions with local binary pattern operator: Preliminary results},\n type = {inproceedings},\n year = {2008},\n pages = {18},\n id = {d1ddf83f-776d-3816-b5ce-5cca8493b408},\n created = {2019-09-15T16:34:26.976Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-23T18:20:07.805Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {In this paper we describe a setup and preliminary results of an experiment, where machine vision has been used for emotion analysis based on facial expressions. In the experiment a set of IAPS pictures was shown to the subjects and their responses were measured from a video recording using a spatiotemporal local binary pattern descriptor. The facial expressions were divided into three categories: pleasant, neutral and unpleasant. The classification results obtained are encouraging.},\n bibtype = {inproceedings},\n author = {Laukka, Seppo J and Rantanen, Antti and Zhao, Guoying and Taini, Matti and Heikkilä, Janne},\n booktitle = {Proceedings of EHTI'08: The First Finnish Symposium on Emotions and Human-Technology Interaction}\n}

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\n \n\n \n \n \n \n \n Face tracking for spatially aware mobile user interfaces.\n \n \n \n\n\n \n Hannuksela, J.; Sangi, P.; Turtinen, M.; and Heikkilä, J.\n\n\n \n\n\n\n In Image and Signal Processing. ICISP 2008. Lecture Notes in Computer Science, volume 5099 LNCS, pages 405-412, 2008. Springer, Berlin, Heidelberg\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

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@inproceedings{\n title = {Face tracking for spatially aware mobile user interfaces},\n type = {inproceedings},\n year = {2008},\n keywords = {Facial feature extraction,Motion analysis,Pose estimation},\n pages = {405-412},\n volume = {5099 LNCS},\n publisher = {Springer, Berlin, Heidelberg},\n id = {61826fad-e014-3f3c-87f4-177db1fa1b72},\n created = {2019-09-15T16:34:27.058Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-23T18:20:08.412Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {This paper introduces a new face tracking approach for controlling user interfaces in hand-held mobile devices. The proposed method detects the face and the eyes of the user by employing a method based on local texture features and boosting. An extended Kalman filter combines local motion features extracted from the face region and the detected eye positions to estimate the 3-D position and orientation of the camera with respect to the face. The camera position is used as an input for the spatially aware user interface. Experimental results on real image sequences captured with a camera-equipped mobile phone validate the feasibility of the method. © 2008 Springer-Verlag.},\n bibtype = {inproceedings},\n author = {Hannuksela, Jari and Sangi, Pekka and Turtinen, Markus and Heikkilä, Janne},\n doi = {10.1007/978-3-540-69905-7_46},\n booktitle = {Image and Signal Processing. ICISP 2008. Lecture Notes in Computer Science}\n}

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\n \n\n \n \n \n \n \n A method for blur and affine invariant object recognition using phase-only bispectrum.\n \n \n \n\n\n \n Ojansivu, V.; and Heikkilä, J.\n\n\n \n\n\n\n In Image Analysis and Recognition. ICIAR 2008. Lecture Notes in Computer Science, volume 5112 LNCS, pages 527-536, 2008. Springer, Berlin, Heidelberg\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {A method for blur and affine invariant object recognition using phase-only bispectrum},\n type = {inproceedings},\n year = {2008},\n pages = {527-536},\n volume = {5112 LNCS},\n publisher = {Springer, Berlin, Heidelberg},\n id = {6e687f0f-1560-3aeb-95c0-b74529a53273},\n created = {2019-09-15T16:34:27.133Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-23T18:20:08.204Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Ojansivu, Ville and Heikkilä, Janne},\n doi = {10.1007/978-3-540-69812-8_52},\n booktitle = {Image Analysis and Recognition. ICIAR 2008. Lecture Notes in Computer Science}\n}

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\n \n\n \n \n \n \n \n Blur and contrast invariant fast stereo matching.\n \n \n \n\n\n \n Pedone, M.; and Heikkilä, J.\n\n\n \n\n\n\n In Advanced Concepts for Intelligent Vision Systems. ACIVS 2008. Lecture Notes in Computer Science, volume 5259 LNCS, pages 883-890, 2008. Springer, Berlin, Heidelberg\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Blur and contrast invariant fast stereo matching},\n type = {inproceedings},\n year = {2008},\n pages = {883-890},\n volume = {5259 LNCS},\n publisher = {Springer, Berlin, Heidelberg},\n id = {57a3957e-cada-3553-83e5-4c5e928072a7},\n created = {2019-09-15T16:34:27.148Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-23T18:20:07.776Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Pedone, Matteo and Heikkilä, Janne},\n doi = {10.1007/978-3-540-88458-3-80},\n booktitle = {Advanced Concepts for Intelligent Vision Systems. ACIVS 2008. Lecture Notes in Computer Science}\n}

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\n \n\n \n \n \n \n \n Measuring and modelling sewer pipes from video.\n \n \n \n\n\n \n Kannala, J.; Brandt, S., S.; and Heikkilä, J.\n\n\n \n\n\n\n Machine Vision and Applications, 19(2): 73-83. 2008.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Measuring and modelling sewer pipes from video},\n type = {article},\n year = {2008},\n keywords = {3D-reconstruction,Modelling,Omnidirectional vision,Structure from motion,Visual inspection},\n pages = {73-83},\n volume = {19},\n id = {4f3db386-a2c9-326b-9738-d45b9a58fda3},\n created = {2019-09-15T16:34:27.192Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-23T18:20:08.638Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {JOUR},\n private_publication = {false},\n abstract = {This article presents a system for the automatic measurement and modelling of sewer pipes. The system recovers the interior shape of a sewer pipe from a video sequence which is acquired by a fish-eye lens camera moving inside the pipe. The approach is based on tracking interest points across successive video frames and posing the general structure-from-motion problem. It is shown that the tracked points can be reliably reconstructed despite the forward motion of the camera. This is achieved by utilizing a fish-eye lens with a wide field of view. The standard techniques for robust estimation of the two- and three-view geometry are modified so that they can be used for calibrated fish-eye lens cameras with a field of view less than 180 degrees. The tubular arrangement of the reconstructed points allows pipe shape estimation by surface fitting. Hence, a method for modelling such surfaces with a locally cylindrical model is proposed. The system is demonstrated with a real sewer video and an error analysis for the recovered structure is presented.},\n bibtype = {article},\n author = {Kannala, Juho and Brandt, Sami S and Heikkilä, Janne},\n doi = {10.1007/s00138-007-0083-1},\n journal = {Machine Vision and Applications},\n number = {2}\n}

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\n \n\n \n \n \n \n \n Adaptive motion-based gesture recognition interface for mobile phones.\n \n \n \n\n\n \n Hannuksela, J.; Barnard, M.; Sangi, P.; and Heikkilä, J.\n\n\n \n\n\n\n In Computer Vision Systems. ICVS 2008. Lecture Notes in Computer Science, volume 5008 LNCS, pages 271-280, 2008. Springer, Berlin, Heidelberg\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@inproceedings{\n title = {Adaptive motion-based gesture recognition interface for mobile phones},\n type = {inproceedings},\n year = {2008},\n keywords = {Finger tracking,Handheld devices,Human-computer interaction,MAP adaptation,Motion estimation},\n pages = {271-280},\n volume = {5008 LNCS},\n publisher = {Springer, Berlin, Heidelberg},\n id = {bd2942e6-8829-33a1-9662-2a9fd59f2bc1},\n created = {2019-09-15T16:34:27.252Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-15T18:07:37.962Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {In this paper, we introduce a new vision based interaction technique for mobile phones. The user operates the interface by simply moving a finger in front of a camera. During these movements the finger is tracked using a method that embeds the Kalman filter and Expectation Maximization (EM) algorithms. Finger movements are interpreted as gestures using Hidden Markov Models (HMMs). This involves first creating a generic model of the gesture and then utilizing unsupervised Maximum a Posteriori (MAP) adaptation to improve the recognition rate for a specific user. Experiments conducted on a recognition task involving simple control commands clearly demonstrate the performance of our approach. © 2008 Springer-Verlag Berlin Heidelberg.},\n bibtype = {inproceedings},\n author = {Hannuksela, Jari and Barnard, Mark and Sangi, Pekka and Heikkilä, Janne},\n doi = {10.1007/978-3-540-79547-6_26},\n booktitle = {Computer Vision Systems. ICVS 2008. Lecture Notes in Computer Science}\n}

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\n \n\n \n \n \n \n \n Constrain propagation for ghost removal in high dynamic range images.\n \n \n \n\n\n \n Pedone, M.; and Heikkilä, J.\n\n\n \n\n\n\n In VISAPP 2008 - 3rd International Conference on Computer Vision Theory and Applications, Proceedings, volume 1, pages 36-41, 2008. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@inproceedings{\n title = {Constrain propagation for ghost removal in high dynamic range images},\n type = {inproceedings},\n year = {2008},\n keywords = {Density estimation,Energy minimization,Image fusion,Motion detection},\n pages = {36-41},\n volume = {1},\n id = {5c7c9464-82d2-35d5-b03c-daada133a481},\n created = {2019-09-15T16:34:27.330Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-15T18:07:37.772Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {Creating high dynamic range images of non-static scenes is currently a challenging task. Carefully preventing strong camera shakes during shooting and performing image-registration before combining the exposures cannot ensure that the resulting hdr image is consistent. This is eventually due to the presence of moving objects in the scene that causes the so called ghosting artifacts. Different approaches have been developed so far in order to reduce the visible effects of ghosts in hdr images. Our iterative method propagates the influences of pixels that have low chances to belong to the static part of the scene through an image-guided energy minimization approach. Results produced with our technique show a significant reduction or total removal of ghosting artifacts.},\n bibtype = {inproceedings},\n author = {Pedone, Matteo and Heikkilä, Janne},\n booktitle = {VISAPP 2008 - 3rd International Conference on Computer Vision Theory and Applications, Proceedings}\n}

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\n \n\n \n \n \n \n \n Blur insensitive texture classification using local phase quantization.\n \n \n \n\n\n \n Ojansivu, V.; and Heikkilä, J.\n\n\n \n\n\n\n In Image and Signal Processing. ICISP 2008. Lecture Notes in Computer Science, volume 5099 LNCS, pages 236-243, 2008. Springer, Berlin, Heidelberg\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Blur insensitive texture classification using local phase quantization},\n type = {inproceedings},\n year = {2008},\n pages = {236-243},\n volume = {5099 LNCS},\n publisher = {Springer, Berlin, Heidelberg},\n id = {2a961a6a-6213-3b68-a891-5087900f867f},\n created = {2019-09-15T16:34:27.521Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-17T18:50:28.903Z},\n read = {true},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {In this paper, we propose a new descriptor for texture classification that is robust to image blurring. The descriptor utilizes phase information computed locally in a window for every image position. The phases of the four low-frequency coefficients are decorrelated and uniformly quantized in an eight-dimensional space. A histogram of the resulting code words is created and used as a feature in texture classification. Ideally, the low-frequency phase components are shown to be invariant to centrally symmetric blur. Although this ideal invariance is not completely achieved due to the finite window size, the method is still highly insensitive to blur. Because only phase information is used, the method is also invariant to uniform illumination changes. According to our experiments, the classification accuracy of blurred texture images is much higher with the new method than with the well-known LBP or Gabor filter bank methods. Interestingly, it is also slightly better for textures that are not blurred.},\n bibtype = {inproceedings},\n author = {Ojansivu, Ville and Heikkilä, Janne},\n doi = {10.1007/978-3-540-69905-7_27},\n booktitle = {Image and Signal Processing. ICISP 2008. Lecture Notes in Computer Science}\n}

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\n \n\n \n \n \n \n \n Scallop: An open peer-to-peer framework for distributed sensor networks.\n \n \n \n\n\n \n Saastamoinen, P.; Huttunen, S.; Takala, V.; Heikkilä, M.; and Heikkilä, J.\n\n\n \n\n\n\n In 2008 2nd ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC 2008, pages 1-9, 2008. IEEE\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@inproceedings{\n title = {Scallop: An open peer-to-peer framework for distributed sensor networks},\n type = {inproceedings},\n year = {2008},\n keywords = {Camera networks,Distributed smart cameras,Peer-to-peer,Software frameworks},\n pages = {1-9},\n publisher = {IEEE},\n id = {b8985e6c-0b27-352a-be11-9d347c262870},\n created = {2019-09-15T16:34:28.264Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-23T18:20:08.409Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {Distributed smart cameras and sensors have been an active area of research in recent years. Most of the research has focused on either the machine vision algorithms or on a specific implementation. There has been less activity on building generic frameworks which allow different algorithms, sensors and distribution methods to be used. This paper presents an open and extendable framework for development of distributed sensor networks with an emphasis on peer-to-peer networking. The user is provided with easy access to sensors and communication channels between distributed nodes, allowing the effort to be focused on the development of machine vision algorithms and their use in distributed environments. The framework was used while implementing a simple demonstration system to test the sensor and network functionality. The system contains processing nodes which receive sensor data from cameras and communicate with each other through a peer-to-peer mesh.},\n bibtype = {inproceedings},\n author = {Saastamoinen, Pekka and Huttunen, Sami and Takala, Valtteri and Heikkilä, Marko and Heikkilä, Janne},\n doi = {10.1109/ICDSC.2008.4635712},\n booktitle = {2008 2nd ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC 2008}\n}

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\n \n\n \n \n \n \n \n Multi-object tracking using binary masks.\n \n \n \n\n\n \n Huttunen, S.; and Heikkilä, J.\n\n\n \n\n\n\n In Proceedings - International Conference on Image Processing, ICIP, pages 2640-2643, 2008. IEEE\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

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@inproceedings{\n title = {Multi-object tracking using binary masks},\n type = {inproceedings},\n year = {2008},\n keywords = {Kalman filter,Object tracking,Soft assignment},\n pages = {2640-2643},\n publisher = {IEEE},\n id = {e8e730fc-6516-322c-9d9e-8e654be8d972},\n created = {2019-09-15T16:34:28.625Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-23T18:20:08.709Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {In this paper, we introduce a new method for tracking multiple objects. The method combines Kalman filtering and the Expectation Maximization (EM) algorithm in a novel way to deal with observations that obey a Gaussian mixture model instead of a unimodal distribution that is assumed by the ordinary Kalman filter. It also involves a new approach to measuring the object locations using a series of morphological operations with binary masks. The benefit of this approach is that soft assignment of the measurements to corresponding objects can be performed automatically using their a posteriori probabilities. This is a general approach for multi-object tracking, and there are basically various ways to segment the objects, but in this paper we use simple color features simply to demonstrate the feasibility of the concept.},\n bibtype = {inproceedings},\n author = {Huttunen, Sami and Heikkilä, Janne},\n doi = {10.1109/ICIP.2008.4712336},\n booktitle = {Proceedings - International Conference on Image Processing, ICIP}\n}

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\n \n\n \n \n \n \n \n Body part segmentation of noisy human silhouette images.\n \n \n \n\n\n \n Barnard, M.; Matilainen, M.; and Heikkilä, J.\n\n\n \n\n\n\n In 2008 IEEE International Conference on Multimedia and Expo, ICME 2008 - Proceedings, pages 1189-1192, 2008. IEEE\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

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@inproceedings{\n title = {Body part segmentation of noisy human silhouette images},\n type = {inproceedings},\n year = {2008},\n keywords = {Body part recognition,Shape context features,Silhouette segmentation},\n pages = {1189-1192},\n publisher = {IEEE},\n id = {3b17c237-6efd-3765-95db-edba1a788a91},\n created = {2019-09-15T16:34:28.811Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-23T18:20:08.331Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {In this paper we propose a solution to the problem of body part segmentation in noisy silhouette images. In developing this solution we revisit the issue of insufficient labeled training data, by investigating how synthetically generated data can be used to train general statistical models for shape classification. In our proposed solution we produce sequences of synthetically generated images, using three dimensional rendering and motion capture information. Each image in these sequences is labeled automatically as it is generated and this labeling is based on the hand labeling of a single initial image.We use shape context features and Hidden Markov Models trained based on this labeled synthetic data. This model is then used to segment silhouettes into four body parts; arms, legs, body and head. Importantly, in all the experiments we conducted the same model is employed with no modification of any parameters after initial training.},\n bibtype = {inproceedings},\n author = {Barnard, Mark and Matilainen, Matti and Heikkilä, Janne},\n doi = {10.1109/ICME.2008.4607653},\n booktitle = {2008 IEEE International Conference on Multimedia and Expo, ICME 2008 - Proceedings}\n}

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\n \n\n \n \n \n \n \n Object recognition and segmentation by non-rigid quasi-dense matching.\n \n \n \n\n\n \n Kannala, J.; Rahtu, E.; Brandt, S., S.; and Heikkila, J.\n\n\n \n\n\n\n In 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pages 1-8, 2008. IEEE\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Object recognition and segmentation by non-rigid quasi-dense matching},\n type = {inproceedings},\n year = {2008},\n pages = {1-8},\n publisher = {IEEE},\n id = {e74b904f-f6f5-3602-b57b-23d53862f052},\n created = {2019-09-15T16:34:29.167Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-23T18:20:08.468Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {In this paper, we present a non-rigid quasi-dense matching method\\nand its application to object recognition and segmentation. The matching\\nmethod is based on the match propagation algorithm which is here\\nextended by using local image gradients for adapting the propagation\\nto smooth non-rigid deformations of the imaged surfaces. The adaptation\\nis based entirely on the local properties of the images and the method\\ncan be hence used in non-rigid image registration where global geometric\\nconstraints are not available. Our approach for object recognition\\nand segmentation is directly built on the quasi-dense matching. The\\nquasi-dense pixel matches between the model and test images are grouped\\ninto geometrically consistent groups using a method which utilizes\\nthe local affine transformation estimates obtained during the propagation.\\nThe number and quality of geometrically consistent matches is used\\nas a recognition criterion and the location of the matching pixels\\ndirectly provides the segmentation. The experiments demonstrate that\\nour approach is able to deal with extensive background clutter, partial\\nocclusion, large scale and viewpoint changes, and notable geometric\\ndeformations.},\n bibtype = {inproceedings},\n author = {Kannala, Juho and Rahtu, Esa and Brandt, Sami S and Heikkila, Janne},\n doi = {10.1109/CVPR.2008.4587472},\n booktitle = {26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR}\n}

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\n \n\n \n \n \n \n \n A distance education system with automatic video source selction and switching.\n \n \n \n\n\n \n Huttunen, S.; Heikkilä, J.; and Silvén, O.\n\n\n \n\n\n\n Advanced Technology for Learning, 5(1): 8. 2008.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{\n title = {A distance education system with automatic video source selction and switching},\n type = {article},\n year = {2008},\n pages = {8},\n volume = {5},\n id = {7446c838-a316-3e02-8ca2-87f29254da09},\n created = {2019-09-15T16:34:29.244Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-23T18:20:08.506Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {JOUR},\n private_publication = {false},\n bibtype = {article},\n author = {Huttunen, S and Heikkilä, J and Silvén, O},\n doi = {10.2316/journal.208.2008.1.208-1054},\n journal = {Advanced Technology for Learning},\n number = {1}\n}

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\n \n\n \n \n \n \n \n Rotation invariant local phase quantization for blur insensitive texture analysis.\n \n \n \n\n\n \n Ojansivu, V.; Rahtu, E.; and Heikkilä, J.\n\n\n \n\n\n\n In Proceedings - International Conference on Pattern Recognition, pages 1-4, 2008. IEEE\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Rotation invariant local phase quantization for blur insensitive texture analysis},\n type = {inproceedings},\n year = {2008},\n pages = {1-4},\n publisher = {IEEE},\n id = {bacd8f53-558a-3050-8385-28346c32f61a},\n created = {2019-09-23T18:20:07.383Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-23T18:20:07.383Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {This paper introduces a rotation invariant extension to the blur insensitive local phase quantization texture descriptor. The new method consists of two stages, the first of which estimates the local characteristic orientation, and the second one extracts a binary descriptor vector. Both steps of the algorithm apply the phase of the locally computed Fourier transform coefficients, which can be shown to be insensitive to centrally symmetric image blurring. The new descriptors are assessed in comparison with the well known texture descriptors, local binary patterns (LBP) and Gabor filtering. The results illustrate that the proposed method has superior performance in those cases where the image contains blur and is slightly better even with sharp images.},\n bibtype = {inproceedings},\n author = {Ojansivu, Ville and Rahtu, Esa and Heikkilä, Janne},\n booktitle = {Proceedings - International Conference on Pattern Recognition}\n}

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\n \n\n \n \n \n \n \n Affine registration using multiscale approach.\n \n \n \n\n\n \n Rahtu, E.; Salo, M.; and Heikkilä, J.\n\n\n \n\n\n\n In Proc. of the Finnish Signal Processing Symposium, FINSIG07, 2007. in: Proc. of the Finnish Signal Processing Symposium, FINSIG07, August 30.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Affine registration using multiscale approach},\n type = {inproceedings},\n year = {2007},\n publisher = {in: Proc. of the Finnish Signal Processing Symposium, FINSIG07, August 30.},\n id = {742209cb-963e-32de-b7cb-6c78b6bcf342},\n created = {2019-09-15T16:34:26.252Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-26T17:24:08.062Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Rahtu, Esa and Salo, Mikko and Heikkilä, Janne},\n booktitle = {Proc. of the Finnish Signal Processing Symposium, FINSIG07}\n}

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\n \n\n \n \n \n \n \n Fast Registration Methods for Super-Resolution Imaging.\n \n \n \n\n\n \n Hannuksela, J.; Väyrynen, J.; Heikkilä, J.; and Sangi, P.\n\n\n \n\n\n\n In Finnish Signal Processing Symposium, 2007. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Fast Registration Methods for Super-Resolution Imaging},\n type = {inproceedings},\n year = {2007},\n id = {0815e2ec-f9d9-31ea-aea5-21b065c42ffd},\n created = {2019-09-15T16:34:26.377Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-26T17:24:07.663Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Hannuksela, Jari and Väyrynen, Jarno and Heikkilä, Janne and Sangi, Pekka},\n booktitle = {Finnish Signal Processing Symposium}\n}

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\n \n\n \n \n \n \n \n Monocular point based pose estimation of artificial markers by using evolutionary computing.\n \n \n \n\n\n \n Heimonen, T.; and Heikkilä, J.\n\n\n \n\n\n\n In Image Analysis. SCIA 2007. Lecture Notes in Computer Science, volume 4522 LNCS, pages 122-131, 2007. Springer, Berlin, Heidelberg\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Monocular point based pose estimation of artificial markers by using evolutionary computing},\n type = {inproceedings},\n year = {2007},\n pages = {122-131},\n volume = {4522 LNCS},\n publisher = {Springer, Berlin, Heidelberg},\n id = {4288709b-cd70-37ed-b4c1-5fa262a7ce84},\n created = {2019-09-15T16:34:26.535Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-26T17:24:07.466Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Heimonen, Teuvo and Heikkilä, Janne},\n booktitle = {Image Analysis. SCIA 2007. Lecture Notes in Computer Science}\n}

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\n \n\n \n \n \n \n \n Nonlinear functionals in the construction of multiscale affine invariants.\n \n \n \n\n\n \n Rahtu, E.; Salo, M.; and Heikkilä, J.\n\n\n \n\n\n\n In Image Analysis. SCIA 2007. Lecture Notes in Computer Science, volume 4522 LNCS, pages 482-491, 2007. Springer, Berlin, Heidelberg\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Nonlinear functionals in the construction of multiscale affine invariants},\n type = {inproceedings},\n year = {2007},\n pages = {482-491},\n volume = {4522 LNCS},\n publisher = {Springer, Berlin, Heidelberg},\n id = {5adb2471-6416-3bf1-90e0-360c1421d05e},\n created = {2019-09-15T16:34:26.810Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-26T17:24:07.661Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Rahtu, Esa and Salo, Mikko and Heikkilä, Janne},\n booktitle = {Image Analysis. SCIA 2007. Lecture Notes in Computer Science}\n}

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\n \n\n \n \n \n \n \n A visual system for hand gesture recognition in human-computer interaction.\n \n \n \n\n\n \n Okkonen, M., A.; Kellokumpu, V.; Pietikäinen, M.; and Heikkilä, J.\n\n\n \n\n\n\n In Image Analysis. SCIA 2007. Lecture Notes in Computer Science, volume 4522 LNCS, pages 709-718, 2007. Springer, Berlin, Heidelberg\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {A visual system for hand gesture recognition in human-computer interaction},\n type = {inproceedings},\n year = {2007},\n pages = {709-718},\n volume = {4522 LNCS},\n publisher = {Springer, Berlin, Heidelberg},\n id = {842756bb-b00f-33e8-87bf-5d9bf0cd95f4},\n created = {2019-09-15T16:34:26.889Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-26T17:24:08.021Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {Visual hand gestures offer an interesting modality for Human-Computer- Interaction (HCI) applications. Gesture recognition and hand tracking, however, are not trivial tasks and real environments set a lot of challenges to algorithms performing such activities. In this paper, a novel combination of techniques is presented for tracking and recognition of hand gestures in real, cluttered environments. In addition to combining existing techniques, a method for locating a hand and segmenting it from an arm in binary silhouettes and a foreground model for color segmentation is proposed. A single hand is tracked with a single camera and the trajectory information is extracted along with recognition of five different gestures. This information is exploited for replacing the operations of a normal computer mouse. The silhouette of the hand is extracted as a combination of different segmentation methods: An adaptive colour model based segmentation is combined with intensity and chromaticity based background subtraction techniques to achieve robust performance in cluttered scenes. An affine-invariant Fourier-descriptor is derived from the silhouette, which is then classified to a hand shape class with support vector machines (SVM). Gestures are recognized as changes in the hand shape with a finite state machine (FSM). © Springer-Verlag Berlin Heidelberg 2007.},\n bibtype = {inproceedings},\n author = {Okkonen, Matti Antero and Kellokumpu, Vili and Pietikäinen, Matti and Heikkilä, Janne},\n booktitle = {Image Analysis. SCIA 2007. Lecture Notes in Computer Science}\n}

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\n \n\n \n \n \n \n \n Blur invariant registration of rotated, scaled and shifted images.\n \n \n \n\n\n \n Ojansivu, V.; and Heikkilä, J.\n\n\n \n\n\n\n In European Signal Processing Conference, pages 1755-1759, 2007. IEEE\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Blur invariant registration of rotated, scaled and shifted images},\n type = {inproceedings},\n year = {2007},\n pages = {1755-1759},\n publisher = {IEEE},\n id = {92a9e944-2ec0-3d77-9e56-fa75f90cd604},\n created = {2019-09-15T16:34:27.017Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-26T17:24:07.876Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {In this paper, we propose a blur invariant image registra-tion method that can be used to register rotated, scaled and shifted images. The method is invariant to centrally symmet-ric blur including linear motion and out of focus blur. The method correlates log-polar sampled phase-only bispectrum slices, which are modified for blur invariance, to estimate ro-tation and scale parameters. Translation parameters are es-timated using a blur invariant version of phase correlation. An additional advantage of using the phase-only bispectrum is the invariance to uniform illumination changes. We present also results of numerical experiments with comparisons to similar registration methods which do not possess blur in-variance properties. The results show that the image regis-tration accuracy of our method is much better when images are blurred.},\n bibtype = {inproceedings},\n author = {Ojansivu, Ville and Heikkilä, Janne},\n booktitle = {European Signal Processing Conference}\n}

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\n \n\n \n \n \n \n \n Global motion estimation using block matching with uncertainty analysis.\n \n \n \n\n\n \n Sangi, P.; Hannuksela, J.; and Heikkilä, J.\n\n\n \n\n\n\n In European Signal Processing Conference, pages 1823-1827, 2007. IEEE\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Global motion estimation using block matching with uncertainty analysis},\n type = {inproceedings},\n year = {2007},\n pages = {1823-1827},\n publisher = {IEEE},\n id = {8cb70f5d-b037-31ba-974e-5381228d63b4},\n created = {2019-09-15T16:34:27.179Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-26T17:24:07.842Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {The paper presents an approach to dominant global motion estimation which is based on estimation of displacements of a small set of blocks. For each block, a robust motion-compensated block matching measure is evaluated for a fixed range of displacements. Analysis of matching measure values provides displacement estimates, and the related uncertainty is also evaluated using a gradient based thresholding method. The results of the uncertainty analysis are then utilized in global motion estimation for outlier analysis and parametric motion model fitting. The performance of the technique is evaluated in experiments which show the usefulness of the approach. © 2007 EURASIP.},\n bibtype = {inproceedings},\n author = {Sangi, Pekka and Hannuksela, Jari and Heikkilä, Janne},\n booktitle = {European Signal Processing Conference}\n}

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\n \n\n \n \n \n \n \n Object recognition using frequency domain blur invariant features.\n \n \n \n\n\n \n Ojansivu, V.; and Heikkilä, J.\n\n\n \n\n\n\n In Image Analysis. SCIA 2007. Lecture Notes in Computer Science, volume 4522 LNCS, pages 243-252, 2007. Springer, Berlin, Heidelberg\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Object recognition using frequency domain blur invariant features},\n type = {inproceedings},\n year = {2007},\n pages = {243-252},\n volume = {4522 LNCS},\n publisher = {Springer, Berlin, Heidelberg},\n id = {798eb06d-94f4-38c9-a292-8b5acf2f24a0},\n created = {2019-09-15T16:34:27.217Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-26T17:24:07.500Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {In this paper, we propose novel blur invariant features for the recognition of objects in images. The features are computed either using the phase-only spectrum or bispectrum of the images and are invariant to centrally symmetric blur, such as linear motion or defocus blur as well as linear illumination changes. The features based on the bispectrum are also invariant to translation, and according to our knowledge they are the only combined blur-translation invariants in the frequency domain. We have compared our features to the blur invariants based on image moments in simulated and real experiments. The results show that our features can recognize blurred images better and, in a practical situation, they are faster to compute using FFT.},\n bibtype = {inproceedings},\n author = {Ojansivu, Ville and Heikkilä, Janne},\n booktitle = {Image Analysis. SCIA 2007. Lecture Notes in Computer Science}\n}

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\n \n\n \n \n \n \n \n A vision based motion interface for mobile phones.\n \n \n \n\n\n \n Barnard, M.; Hannuksela, J.; Sangi, P.; and Heikkil, J.\n\n\n \n\n\n\n In The 5th International Conf. on Computer Vision Systems, 2007. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {A vision based motion interface for mobile phones},\n type = {inproceedings},\n year = {2007},\n id = {b6da8ade-648c-3127-bdad-be510a8bc932},\n created = {2019-09-15T16:34:27.265Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-15T18:07:37.694Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Barnard, Mark and Hannuksela, Jari and Sangi, Pekka and Heikkil, Janne},\n booktitle = {The 5th International Conf. on Computer Vision Systems}\n}

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\n \n\n \n \n \n \n \n Vision-based motion estimation for interaction with mobile devices.\n \n \n \n\n\n \n Hannuksela, J.; Sangi, P.; and Heikkilä, J.\n\n\n \n\n\n\n Computer Vision and Image Understanding, 108(1-2): 188-195. 2007.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Vision-based motion estimation for interaction with mobile devices},\n type = {article},\n year = {2007},\n keywords = {Global motion estimation,Handheld devices,User interfaces},\n pages = {188-195},\n volume = {108},\n id = {74673f95-8d79-30e7-bc13-482d909b2fe2},\n created = {2019-09-15T16:34:27.519Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-15T18:07:37.864Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {JOUR},\n private_publication = {false},\n abstract = {This paper introduces a novel interaction technique for handheld mobile devices which enables the user interface to be controlled by the motion of the user's hand. A feature-based approach is proposed for global motion estimation that exploits gradient measures for both feature selection and feature motion uncertainty analysis. A voting-based scheme is presented for outlier removal. A Kalman filter is applied for smoothing motion trajectories. A fixed-point implementation of the method was developed due to the lack of floating-point hardware. Experiments testify the effectiveness of the approach on a camera-enabled mobile phone. © 2007 Elsevier Inc. All rights reserved.},\n bibtype = {article},\n author = {Hannuksela, Jari and Sangi, Pekka and Heikkilä, Janne},\n doi = {10.1016/j.cviu.2006.10.014},\n journal = {Computer Vision and Image Understanding},\n number = {1-2}\n}

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\n \n\n \n \n \n \n \n A new rotation search for dependent rate-distortion optimization in video coding.\n \n \n \n\n\n \n Toivonen, T.; Merritt, L.; Ojansivu, V.; and Heikkilä, J.\n\n\n \n\n\n\n In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, volume 1, pages I-1165, 2007. IEEE\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

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@inproceedings{\n title = {A new rotation search for dependent rate-distortion optimization in video coding},\n type = {inproceedings},\n year = {2007},\n keywords = {Motion estimation,Optimization methods,Rate-distortion optimization},\n pages = {I-1165},\n volume = {1},\n publisher = {IEEE},\n id = {faf76fdf-aefe-31f0-961b-4fb5001e78aa},\n created = {2019-09-15T16:34:28.585Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-26T17:24:08.101Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Toivonen, Tuukka and Merritt, Loren and Ojansivu, Ville and Heikkilä, Janne},\n doi = {10.1109/ICASSP.2007.366120},\n booktitle = {ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings}\n}

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\n \n\n \n \n \n \n \n Motion-based finger tracking for user interaction with mobile devices.\n \n \n \n\n\n \n Hannuksela, J.; Huttunen, S.; Sangi, P.; and Heikkilä, J.\n\n\n \n\n\n\n In IET Conference Publications, 2007. \n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@inproceedings{\n title = {Motion-based finger tracking for user interaction with mobile devices},\n type = {inproceedings},\n year = {2007},\n keywords = {EM algorithm,Kalman filter,Motion estimation,Motion features},\n issue = {534 CP},\n id = {4a3c2457-3242-376e-a852-f5face6b63b8},\n created = {2019-09-15T16:34:28.918Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-26T17:24:07.676Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {JOUR},\n private_publication = {false},\n abstract = {A new motion-based tracking algorithm for user interaction with hand-held mobile devices is presented. The idea is to allow mobile phone users to control the device simply by moving a finger in front of a camera. A novel combination of Kalman filtering and the expectation maximization (EM) algorithm is utilized for estimation of two distinct motion components corresponding to the camera motion and the finger motion. The estimation is based on motion features, which are effectively extracted from the scene for each image frame. The performance of the technique is evaluated in experiments which show the usefulness of the approach. The method can be applied also when some conventional finger tracking techniques such as color segmentation and background subtraction cannot be used.},\n bibtype = {inproceedings},\n author = {Hannuksela, Jari and Huttunen, Sami and Sangi, Pekka and Heikkilä, J.},\n doi = {10.1049/cp:20070038},\n booktitle = {IET Conference Publications}\n}

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\n \n\n \n \n \n \n \n Document image mosaicing with mobile phones.\n \n \n \n\n\n \n Hannuksela, J.; Sangi, P.; Heikkilä, J.; Liu, X.; and Doermann, D.\n\n\n \n\n\n\n In Proceedings - 14th International conference on Image Analysis and Processing, ICIAP 2007, pages 575-580, 2007. IEEE\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Document image mosaicing with mobile phones},\n type = {inproceedings},\n year = {2007},\n pages = {575-580},\n publisher = {IEEE},\n id = {a441c0c8-965c-3111-ae49-00ab27104e26},\n created = {2019-09-15T16:34:29.028Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-26T17:24:07.505Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {This paper presents a novel user interaction concept for document image scanning with mobile phones. A high resolution mosaic image is constructed in two main stages. Firstly, online camera motion estimation is applied to the phone to assist the user to capture small image patches of the document page. Automatic image stitching process with the help of estimated device motion is carried out to reconstruct the full view of the document. Experiments on document images captured and processed with mosaicing software clearly show the feasibility of the approach. © 2007 IEEE.},\n bibtype = {inproceedings},\n author = {Hannuksela, Jari and Sangi, Pekka and Heikkilä, Janne and Liu, Xu and Doermann, David},\n doi = {10.1109/ICIAP.2007.4362839},\n booktitle = {Proceedings - 14th International conference on Image Analysis and Processing, ICIAP 2007}\n}

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\n \n\n \n \n \n \n \n A method for blur and similarity transform invariant object recognition.\n \n \n \n\n\n \n Ojansivu, V.; and Heikkilä, J.\n\n\n \n\n\n\n In Proceedings - 14th International conference on Image Analysis and Processing, ICIAP 2007, pages 583-588, 2007. IEEE\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {A method for blur and similarity transform invariant object recognition},\n type = {inproceedings},\n year = {2007},\n pages = {583-588},\n publisher = {IEEE},\n id = {18d0324c-173b-3dab-b69f-d9fe67047886},\n created = {2019-09-15T16:34:29.130Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-26T17:24:07.960Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Ojansivu, Ville and Heikkilä, Janne},\n doi = {10.1109/ICIAP.2007.4362840},\n booktitle = {Proceedings - 14th International conference on Image Analysis and Processing, ICIAP 2007}\n}

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\n \n\n \n \n \n \n \n \n Image Registration Using Blur-Invariant Phase Correlation.\n \n \n \n \n\n\n \n Ojansivu, V.; and Heikkilä, J.\n\n\n \n\n\n\n IEEE Signal Processing Letters, 14(7): 449-452. 7 2007.\n \n\n\n\n
\n\n\n\n \n \n $\"Image$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Image Registration Using Blur-Invariant Phase Correlation},\n type = {article},\n year = {2007},\n keywords = {Fourier transform,Image alignment,Image blurring},\n pages = {449-452},\n volume = {14},\n websites = {http://ieeexplore.ieee.org/document/4244491/},\n month = {7},\n id = {010ce6db-e2b0-3555-9950-ecd8f072251c},\n created = {2019-09-15T16:34:29.206Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-26T17:24:07.838Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {JOUR},\n private_publication = {false},\n abstract = {<para> In this paper, we propose an image registration method, which is invariant to centrally symmetric blur. The method utilizes the phase of the images and has its roots on phase correlation (PC) registration. We show how the even powers of the normalized Fourier transform of an image are invariant to centrally symmetric blur, such as motion or out-of-focus blur. We then use these results to propose blur-invariant phase correlation. The method has been compared to PC registration with excellent results. With a subpixel extension of PC registration, the method achieves subpixel accuracy for even heavily blurred images. </para> %Z %U %+ %^},\n bibtype = {article},\n author = {Ojansivu, Ville and Heikkilä, Janne},\n doi = {10.1109/LSP.2006.891338},\n journal = {IEEE Signal Processing Letters},\n number = {7}\n}

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\n \n\n \n \n \n \n \n Face and eye detection for person authentication in mobile phones.\n \n \n \n\n\n \n Hadid, A.; Heikkilä, J.; Silven, O.; and Pietikäinen, M.\n\n\n \n\n\n\n In 2007 1st ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC, 2007. \n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@inproceedings{\n title = {Face and eye detection for person authentication in mobile phones},\n type = {inproceedings},\n year = {2007},\n keywords = {Biometrics,Face detection,Mobile phone,Video camera},\n id = {e422967f-06a2-3796-80b2-d8c5ba745b0a},\n created = {2019-11-14T11:05:18.708Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-11-14T11:05:18.708Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n private_publication = {true},\n abstract = {Computer vision applications for mobile phones are gaining increasing attention due to several practical needs resulting from the popularity of digital cameras in today's mobile phones. In this work, we consider the task of face detection and authentication in mobile phones and experimentally analyze a face authentication scheme using Haar-like features with Ad-aBoost for face and eye detection, and Local Binary Pattern (LBP) approach for face authentication. For comparison, another approach to face detection using skin color for fast processing is also considered and implemented. Despite the limited CPU and memory capabilities of today's mobile phones, our experimental results show good face detection performance and average authentication rates of 82% for small-sized faces (40x40 pixels) and 96% for faces of 80 x 80 pixels. The system is running at 2 frames per second for images of 320 x 240 pixels. The obtained results are very promising and assess the feasibility of face authentication in mobile phones. Directions for further enhancing the performance of the system are also discussed. ©2007 IEEE.},\n bibtype = {inproceedings},\n author = {Hadid, A. and Heikkilä, J.Y. and Silven, O. and Pietikäinen, M.},\n doi = {10.1109/ICDSC.2007.4357512},\n booktitle = {2007 1st ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC}\n}

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\n \n 2006\n \n \n (12)\n \n \n

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\n \n\n \n \n \n \n \n Video filtering with Fermat number theoretic transforms using residue number system.\n \n \n \n\n\n \n Toivonen, T.; and Heikkilä, J.\n\n\n \n\n\n\n IEEE Transactions on Circuits and Systems for Video Technology, 16(1): 92-101. 2006.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Video filtering with Fermat number theoretic transforms using residue number system},\n type = {article},\n year = {2006},\n keywords = {Binary code,Fermat number theoretic transforms,Image convolutions,Residue number system,Video convolutions,Video filtering},\n pages = {92-101},\n volume = {16},\n id = {282a135e-48fb-3256-abc0-ccda8f660a18},\n created = {2019-09-15T16:34:26.039Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-26T17:24:07.492Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {JOUR},\n private_publication = {false},\n abstract = {We investigate image and video convolutions based on Fermat number transform (FNT) modulo q=2M+1 where M is an integer power of two. These transforms are found to be ideal for image convolutions, except that the choices for the word length, restricted by the transform modulus, are rather limited. We discuss two methods to overcome this limitation. First, we allow M to be an arbitrary integer. This gives much wider variety in possible moduli, at the cost of decreased transform length of 16 or 32 points for M1=216+1 and q2=28+1, which allow transforms up to 256 points with a dynamic range of about 24 bits. We design an efficient reconstruction circuit based on mixed radix conversion for converting the result from diminished-1 RNS into normal binary code. The circuit is implemented in VHDL and found to be very small in area. We also discuss the necessary steps in performing convolutions with the GFNT and evaluate the integrated circuit implementation cost for various elementary operations.},\n bibtype = {article},\n author = {Toivonen, Tuukka and Heikkilä, Janne},\n doi = {10.1109/TCSVT.2005.858612},\n journal = {IEEE Transactions on Circuits and Systems for Video Technology},\n number = {1}\n}

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\n \n\n \n \n \n \n \n \n A new convexity measure based on a probabilistic interpretation of images.\n \n \n \n \n\n\n \n Rahtu, E.; Salo, M.; and Heikkilä, J.\n\n\n \n\n\n\n IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(9): 1501-1512. 9 2006.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {A new convexity measure based on a probabilistic interpretation of images},\n type = {article},\n year = {2006},\n keywords = {Affine invariance,Object classification,Shape analysis},\n pages = {1501-1512},\n volume = {28},\n websites = {http://ieeexplore.ieee.org/document/1661551/},\n month = {9},\n id = {cec9ccac-a402-3723-aa5a-d5776da7cbba},\n created = {2019-09-15T16:34:26.040Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-26T17:24:08.133Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {JOUR},\n private_publication = {false},\n abstract = {In this paper, we present a novel convexity measure for object shape analysis. The proposed method is based on the idea of generating pairs of points from a set and measuring the probability that a point dividing the corresponding line segments belongs to the same set. The measure is directly applicable to image functions representing shapes and also to gray-scale images which approximate image binarizations. The approach introduced gives rise to a variety of convexity measures which make it possible to obtain more information about the object shape. The proposed measure turns out to be easy to implement using the Fast Fourier Transform and we will consider this in detail. Finally, we illustrate the behavior of our measure in different situations and compare it to other similar ones.},\n bibtype = {article},\n author = {Rahtu, Esa and Salo, Mikko and Heikkilä, Janne},\n doi = {10.1109/TPAMI.2006.175},\n journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},\n number = {9}\n}

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\n \n\n \n \n \n \n \n A Vision-Based Approach for Controlling User Interfaces of Mobile Devices.\n \n \n \n\n\n \n Hannuksela, J.; Sangi, P.; and Heikkilä, J.\n\n\n \n\n\n\n In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)-Workshops, pages 71-71, 2006. IEEE\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {A Vision-Based Approach for Controlling User Interfaces of Mobile Devices},\n type = {inproceedings},\n year = {2006},\n pages = {71-71},\n publisher = {IEEE},\n id = {9d0e00ba-73f8-366d-86e1-b5cccdfa7044},\n created = {2019-09-15T16:34:26.126Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-26T17:24:07.342Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {We introduce a novel user interface solution for mobile devices which enables the display to be controlled by the motion of the users hand. A feature-based approach is proposed for dominant global motion estimation that exploits gradient measures for both feature selection and motion uncertainty analysis. We also present a voting-based scheme for outlier removal. A Kalman filter is utilized for smoothing motion trajectories. A fixed-point implementation of the method was made on a mobile device platform that sets computational restrictions for the algorithms used. Experiments with synthetic and real image sequences show the effectiveness of the method and demonstrate the practicality of the approach in a smartphone.},\n bibtype = {inproceedings},\n author = {Hannuksela, Jari and Sangi, Pekka and Heikkilä, Janne},\n doi = {10.1109/cvpr.2005.401},\n booktitle = {2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)-Workshops}\n}

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\n \n\n \n \n \n \n \n Reduced frame quantization in video coding.\n \n \n \n\n\n \n Toivonen, T.; and Heikkilä, J.\n\n\n \n\n\n\n In Visual Content Processing and Representation. VLBV 2005. Lecture Notes in Computer Science, volume 3893 LNCS, pages 61-67, 2006. Springer, Berlin, Heidelberg\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Reduced frame quantization in video coding},\n type = {inproceedings},\n year = {2006},\n pages = {61-67},\n volume = {3893 LNCS},\n publisher = {Springer, Berlin, Heidelberg},\n id = {b9bd407f-a085-34ab-bdcc-254e6962b37d},\n created = {2019-09-15T16:34:26.502Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-26T17:24:07.650Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Toivonen, Tuukka and Heikkilä, Janne},\n doi = {10.1007/11738695_9},\n booktitle = {Visual Content Processing and Representation. VLBV 2005. Lecture Notes in Computer Science}\n}

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\n \n\n \n \n \n \n \n Motion blur concealment of digital video using invariant features.\n \n \n \n\n\n \n Ojansivu, V.; and Heikkilä, J.\n\n\n \n\n\n\n In Advanced Concepts for Intelligent Vision Systems. ACIVS 2006. Lecture Notes in Computer Science, volume 4179 LNCS, pages 35-45, 2006. Springer, Berlin, Heidelberg\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Motion blur concealment of digital video using invariant features},\n type = {inproceedings},\n year = {2006},\n pages = {35-45},\n volume = {4179 LNCS},\n publisher = {Springer, Berlin, Heidelberg},\n id = {c8fe2209-1b9a-3710-90a8-019ef034bb60},\n created = {2019-09-15T16:34:26.937Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-26T17:24:08.198Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Ojansivu, Ville and Heikkilä, Janne},\n booktitle = {Advanced Concepts for Intelligent Vision Systems. ACIVS 2006. Lecture Notes in Computer Science}\n}

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\n \n\n \n \n \n \n \n A new affine invariant image transform based on ridgelets.\n \n \n \n\n\n \n Rahtu, E.; Heikkil??, J.; and Salo, M.\n\n\n \n\n\n\n In BMVC 2006 - Proceedings of the British Machine Vision Conference 2006, pages 1039-1048, 2006. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {A new affine invariant image transform based on ridgelets},\n type = {inproceedings},\n year = {2006},\n pages = {1039-1048},\n id = {6bd45eef-d050-3439-bcce-009db9e3e25d},\n created = {2019-09-15T16:34:27.013Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-26T17:24:07.827Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {In this paper we present a new affine invariant image transform, based on ridgelets. The proposed transform is directly applicable to segmented image patches. The new method has some similarities with the previously proposed Multiscale Autoconvolution, but it will offer a more general framework and possibilities for variations. The obtained transform coefficients can be used in affine invariant pattern classification, and as shown in the experiments, already a small subset of them is enough for reliable recognition of complex patterns. The new method is assessed in several experiments and it is observed to perform well under many nonaffine distortions.},\n bibtype = {inproceedings},\n author = {Rahtu, Esa and Heikkil??, Janne and Salo, Mikko},\n booktitle = {BMVC 2006 - Proceedings of the British Machine Vision Conference 2006}\n}

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\n \n\n \n \n \n \n \n Multiscale autoconvolution histograms for affine invariant pattern recognition.\n \n \n \n\n\n \n Rahtu, E.; Salo, M.; and Heikkilä, J.\n\n\n \n\n\n\n In BMVC 2006 - Proceedings of the British Machine Vision Conference 2006, pages 1059-1068, 2006. Citeseer\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Multiscale autoconvolution histograms for affine invariant pattern recognition},\n type = {inproceedings},\n year = {2006},\n pages = {1059-1068},\n publisher = {Citeseer},\n id = {5c164d00-9db9-316b-b9c3-94ff05dfbb48},\n created = {2019-09-15T16:34:27.055Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-26T17:24:07.856Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {In this paper we present a new way of producing affine invariant histograms from images. The approach is based on a probabilistic interpretation of the image function as in the multiscale autoconvolution (MSA) transform, but the histograms extract much more information of the image than traditional MSA. The new histograms can be considered as generalizations of the image gray scale histogram, encoding also the spatial information. It turns out that the proposed method can be efficiently computed using the Fast Fourier Transform, and it will be shown to have essentially the same computational load as MSA. The experiments performed indicate that the new invariants are capable of reliable classification of complex patterns, outperforming MSA and many other methods.},\n bibtype = {inproceedings},\n author = {Rahtu, Esa and Salo, Mikko and Heikkilä, Janne},\n booktitle = {BMVC 2006 - Proceedings of the British Machine Vision Conference 2006}\n}

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\n \n\n \n \n \n \n \n Generalized affine moment invariants for object recognition.\n \n \n \n\n\n \n Rahtu, E.; Salo, M.; Heikkilä, J.; and Flusser, J.\n\n\n \n\n\n\n In Proceedings - International Conference on Pattern Recognition, volume 2, pages 634-637, 2006. IEEE\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Generalized affine moment invariants for object recognition},\n type = {inproceedings},\n year = {2006},\n pages = {634-637},\n volume = {2},\n publisher = {IEEE},\n id = {085b6725-60b0-31f4-8af9-99b4af673539},\n created = {2019-09-15T16:34:27.290Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-15T18:07:37.966Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {This paper introduces a new way of extracting affine invariant features from image functions. The presented approach is based on combining affine moment invariants (AMI) with multiscale invariants, in particular multiscale auto convolution (MSA) and spatial multiscale affine invariants (SMA). Our approach includes all of these invariants as special cases, but also makes it possible to construct new ones. According to the performed experiments the introduced features provide discriminating information for affine invariant object classification, clearly outperforming standard AMI, MSA, and SMA},\n bibtype = {inproceedings},\n author = {Rahtu, Esa and Salo, Mikko and Heikkilä, Janne and Flusser, Jan},\n doi = {10.1109/ICPR.2006.599},\n booktitle = {Proceedings - International Conference on Pattern Recognition}\n}

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\n \n\n \n \n \n \n \n Algorithms for computing a planar homography from conics in correspondence.\n \n \n \n\n\n \n Kannala, J.; Salo, M.; and Heikkilä, J.\n\n\n \n\n\n\n In BMVC 2006 - Proceedings of the British Machine Vision Conference 2006, pages 77-86, 2006. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Algorithms for computing a planar homography from conics in correspondence},\n type = {inproceedings},\n year = {2006},\n pages = {77-86},\n id = {7506276a-385f-35dc-a88b-6e53a6b52c7a},\n created = {2019-09-15T16:34:27.339Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-15T18:07:37.786Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Kannala, Juho and Salo, Mikko and Heikkilä, Janne},\n booktitle = {BMVC 2006 - Proceedings of the British Machine Vision Conference 2006}\n}

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\n \n\n \n \n \n \n \n Motion-based handwriting recognition for mobile interaction.\n \n \n \n\n\n \n Hannuksela, J.; Sangi, P.; and Heikkilä, J.\n\n\n \n\n\n\n In Proceedings - International Conference on Pattern Recognition, volume 4, pages 397-400, 2006. IEEE\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Motion-based handwriting recognition for mobile interaction},\n type = {inproceedings},\n year = {2006},\n pages = {397-400},\n volume = {4},\n publisher = {IEEE},\n id = {6f253ee8-1cf3-351c-a026-84de26f07f49},\n created = {2019-09-15T16:34:28.703Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-26T17:24:08.212Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {This paper presents a new interaction technique for camera-enabled mobile devices. The handheld device can be used for writing just by moving the device. In our method, interframe dominant motion is estimated from images, and the discrete cosine transform is used for computing discriminating features from motion trajectories. The k-nearest neighbor rule is applied for classification. A real-time implementation of the method was developed for a mobile phone. In experiments, recognition rates ranging from 92% to 98% were achieved, which testifies to the practicality of our approach. © 2006 IEEE.},\n bibtype = {inproceedings},\n author = {Hannuksela, Jari and Sangi, Pekka and Heikkilä, Janne},\n doi = {10.1109/ICPR.2006.817},\n booktitle = {Proceedings - International Conference on Pattern Recognition}\n}

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\n \n\n \n \n \n \n \n An active head tracking system for distance education and videoconferencing applications.\n \n \n \n\n\n \n Huttunen, S.; and Heikkilä, J.\n\n\n \n\n\n\n In Proceedings - IEEE International Conference on Video and Signal Based Surveillance 2006, AVSS 2006, pages 30, 2006. IEEE\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {An active head tracking system for distance education and videoconferencing applications},\n type = {inproceedings},\n year = {2006},\n pages = {30},\n publisher = {IEEE},\n id = {8e9672a2-898b-37b1-bbdd-d6876494ad3d},\n created = {2019-09-15T16:34:28.743Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-26T17:24:07.485Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {We present a system for automatic head tracking with a single pan-tilt-zoom (PTZ) camera. In distance education the PTZ tracking system developed can be used to follow a teacher actively when s/he moves in the classroom. In other videoconferencing applications the system can be utilized to provide a close-up view of the person all the time. Since the color features used in tracking are selected and updated online, the system can adapt to changes rapidly. The information received from the tracking module is used to actively control the PTZ camera in order to keep the person in the camera view. In addition, the system implemented is able to recover from erroneous situations. Preliminary experiments indicate that the PTZ system can perform well under different lighting conditions and large scale changes.},\n bibtype = {inproceedings},\n author = {Huttunen, Sami and Heikkilä, Janne},\n doi = {10.1109/AVSS.2006.19},\n booktitle = {Proceedings - IEEE International Conference on Video and Signal Based Surveillance 2006, AVSS 2006}\n}

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\n \n\n \n \n \n \n \n \n Improved unsymmetric-cross multi-hexagon-grid search algorithm for fast block motion estimation.\n \n \n \n \n\n\n \n Toivonen, T.; and Heikkilä, J.\n\n\n \n\n\n\n In Proceedings - International Conference on Image Processing, ICIP, pages 2369-2372, 10 2006. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"Improved$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n\n\n\n

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@inproceedings{\n title = {Improved unsymmetric-cross multi-hexagon-grid search algorithm for fast block motion estimation},\n type = {inproceedings},\n year = {2006},\n keywords = {Motion compensation,Video coding},\n pages = {2369-2372},\n websites = {http://ieeexplore.ieee.org/document/4107043/},\n month = {10},\n publisher = {IEEE},\n id = {58a21180-1888-3113-8140-8611793517c2},\n created = {2019-09-15T16:34:28.820Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-26T17:24:08.012Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Toivonen, Tuukka and Heikkilä, Janne},\n doi = {10.1109/ICIP.2006.312902},\n booktitle = {Proceedings - International Conference on Image Processing, ICIP}\n}

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\n \n 2005\n \n \n (7)\n \n \n

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\n \n\n \n \n \n \n \n Affine invariant pattern recognition using multiscale autoconvolution.\n \n \n \n\n\n \n Rahtu, E.; Salo, M.; and Heikkilä, J.\n\n\n \n\n\n\n IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(6): 908-918. 2005.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Affine invariant pattern recognition using multiscale autoconvolution},\n type = {article},\n year = {2005},\n keywords = {Affine invariance,Affine invariant features,Image transforms,Object recognition,Pattern classification,Target identification},\n pages = {908-918},\n volume = {27},\n id = {f9eaf30b-8d21-31dd-a217-08998eedd73f},\n created = {2019-09-15T16:34:26.130Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-26T17:24:08.040Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {JOUR},\n private_publication = {false},\n abstract = {This paper presents a new affine invariant image transform called multiscale autoconvolution (MSA). The proposed transform is based on a probabilistic interpretation of the image function. The method is directly applicable to isolated objects and doe...},\n bibtype = {article},\n author = {Rahtu, Esa and Salo, Mikko and Heikkilä, Janne},\n doi = {10.1109/TPAMI.2005.111},\n journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},\n number = {6}\n}

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\n \n\n \n \n \n \n \n \n A New Efficient Method for Producing Global Affine Invariants.\n \n \n \n \n\n\n \n Rahtu, E.; Salo, M.; and Heikkilä, J.\n\n\n \n\n\n\n In Image Analysis and Processing – ICIAP 2005. ICIAP 2005. Lecture Notes in Computer Science, volume 3617 LNCS, pages 407-414, 2005. Springer, Berlin, Heidelberg\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {A New Efficient Method for Producing Global Affine Invariants},\n type = {inproceedings},\n year = {2005},\n pages = {407-414},\n volume = {3617 LNCS},\n websites = {http://link.springer.com/10.1007/11553595_50},\n publisher = {Springer, Berlin, Heidelberg},\n id = {7e4bd018-65bc-3fc4-8851-89faadc3a403},\n created = {2019-09-15T16:34:27.376Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-16T06:40:23.684Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Rahtu, Esa and Salo, Mikko and Heikkilä, Janne},\n doi = {10.1007/11553595_50},\n booktitle = {Image Analysis and Processing – ICIAP 2005. ICIAP 2005. Lecture Notes in Computer Science}\n}

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\n \n\n \n \n \n \n \n A new method for affine registration of images and point sets.\n \n \n \n\n\n \n Kannala, J.; Rahtu, E.; Heikkilä, J.; and Salo, M.\n\n\n \n\n\n\n In Image Analysis. SCIA 2005. Lecture Notes in Computer Science, volume 3540, pages 224-234, 2005. Springer, Berlin, Heidelberg\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {A new method for affine registration of images and point sets},\n type = {inproceedings},\n year = {2005},\n pages = {224-234},\n volume = {3540},\n publisher = {Springer, Berlin, Heidelberg},\n id = {76c9fa49-4089-3817-a872-0a6c07cd6192},\n created = {2019-09-15T16:34:27.473Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-17T18:51:18.469Z},\n read = {true},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {In this paper we propose a novel method for affine registration of images and point patterns. The method is non-iterative and it directly utilizes the intensity distribution of the images or the spatial distribution of points in the patterns. The method can be used to align images of isolated objects or sets of 2D and 3D points. For Euclidean and similarity transformations the additional contraints can be easily embedded in the algorithm. The main advantage of the proposed method is its efficiency since the computational complexity is only linearly proportional to the number of pixels in the images (or to the number of points in the sets).In the experiments we have compared our method with some other non-feature-based registration methods and investigated its robustness. The experiments show that the proposed method is relatively robust so that it can be applied in practical circumstances.},\n bibtype = {inproceedings},\n author = {Kannala, Juho and Rahtu, Esa and Heikkilä, Janne and Salo, Mikko},\n booktitle = {Image Analysis. SCIA 2005. Lecture Notes in Computer Science}\n}

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\n \n\n \n \n \n \n \n Human activity recognition using sequences of postures.\n \n \n \n\n\n \n Kellokumpu, V.; Pietikäinen, M.; and Heikkilä, J.\n\n\n \n\n\n\n In Proceedings of the 9th IAPR Conference on Machine Vision Applications, MVA 2005, pages 570-573, 2005. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Human activity recognition using sequences of postures},\n type = {inproceedings},\n year = {2005},\n pages = {570-573},\n id = {8d1c9759-8ccd-3c1d-b62d-36f0c38d589e},\n created = {2019-09-15T16:34:27.559Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-15T17:49:16.218Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {Univers ity of Oulu kello@ ee.oulu.fi M a tti Pietikä inen M a c hine Vis ion Group P .O. Box 4500 FIN-90014 Univers ity of Oulu mkp@ ee.oulu.fi Ja nne Heikkilä M a c hine Vis ion Group P .O. Box 4500 FIN-90014 Univers ity of Oulu j th@ ee.oulu.fi Abstract This paper presents a system, which is able to recognize 15 dif f erent continuous human activ ities in real-time using a single stationary camera as input. The system can rec-ognize activ ities such as raising or wav ing hand(s) , sitting down and bending down. The recognition is based on de-scribing activ ities as a continuous sequence of discrete postures, which are deriv ed f rom af f ine inv ariant descrip-tors. Using af f ine inv ariant descriptors makes our system robust against such dif f erences in camera locations as distance f rom the obj ect and change in v iewing direction as these dif f erences can be considered to hav e the af f ect of near af f ine transf ormations as human silhouettes are con-sidered.},\n bibtype = {inproceedings},\n author = {Kellokumpu, Vili and Pietikäinen, Matti and Heikkilä, Janne},\n booktitle = {Proceedings of the 9th IAPR Conference on Machine Vision Applications, MVA 2005}\n}

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\n \n\n \n \n \n \n \n A likelihood function for block-based motion analysis.\n \n \n \n\n\n \n Sangi, P.; Heikkilä, J.; and Silvén, O.\n\n\n \n\n\n\n In Proceedings - International Conference on Image Processing, ICIP, volume 1, pages 1085-1088, 2005. IEEE\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {A likelihood function for block-based motion analysis},\n type = {inproceedings},\n year = {2005},\n pages = {1085-1088},\n volume = {1},\n publisher = {IEEE},\n id = {32fba83a-4a95-3dcb-968c-ca4c03ade169},\n created = {2019-09-15T16:34:28.509Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-26T17:24:07.594Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {In this paper, the computation of likelihood of block motion candidates is considered. The method is based on the evaluation of the sum of squared differences (SSD) measure for local displacements and probabilistic interpretation of these values using local gradient information. Simulated motion data is used to estimate parameters of conditional SSD distributions. The application of our novel likelihood function is demonstrated in a task of dominant motion estimation, where particle filtering is used to maintain a set of global motion hypotheses. In this task, the block motion likelihood function is used as a basis for hypothesis testing, which provides a means for evaluating global motion hypotheses. © 2005 IEEE.},\n bibtype = {inproceedings},\n author = {Sangi, Pekka and Heikkilä, Janne and Silvén, Olli},\n doi = {10.1109/ICIP.2005.1529943},\n booktitle = {Proceedings - International Conference on Image Processing, ICIP}\n}

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\n \n\n \n \n \n \n \n Affine registration with multi-scale autoconvolution.\n \n \n \n\n\n \n Kannala, J.; Rahtu, E.; and Heikkilä, J.\n\n\n \n\n\n\n In Proceedings - International Conference on Image Processing, ICIP, volume 3, pages 1064-1067, 2005. IEEE\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Affine registration with multi-scale autoconvolution},\n type = {inproceedings},\n year = {2005},\n pages = {1064-1067},\n volume = {3},\n publisher = {IEEE},\n id = {81f64ba6-907e-3e27-96ea-3f4dc3b9139f},\n created = {2019-09-15T16:34:28.772Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-26T17:24:07.767Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {In this paper we propose a novel method for the recovery of affine transformation parameters between two images. Registration is achieved without separate feature extraction by directly utilizing the intensity distribution of the images. The method can also be used for matching point sets under affine transformations. Our approach is based on the same probabilistic interpretation of the image function as the recently introduced Multi-Scale Autoconvolution (MSA) transform. Here we describe how the framework may be used in image registration and present two variants of the method for practical implementation. The proposed method is experimented with binary and grayscale images and compared with other non-feature-based registration methods. The experiments show that the new method can efficiently align images of isolated objects and is relatively robust. © 2005 IEEE.},\n bibtype = {inproceedings},\n author = {Kannala, Juho and Rahtu, Esa and Heikkilä, Janne},\n doi = {10.1109/ICIP.2005.1530579},\n booktitle = {Proceedings - International Conference on Image Processing, ICIP}\n}

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\n \n\n \n \n \n \n \n Camera-based control for a distance education system.\n \n \n \n\n\n \n Huttunen, S.; Heikkilä, J.; and Silvén, O.\n\n\n \n\n\n\n In Proceedings of the IASTED International Conference on Education and Technology, ICET 2005, volume 2005, pages 154-159, 2005. in: Proc. IASTED International Conference on Education and Technology (ICET …\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

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@inproceedings{\n title = {Camera-based control for a distance education system},\n type = {inproceedings},\n year = {2005},\n keywords = {Human-computer interfaces,Instructional technology,Machine vision},\n pages = {154-159},\n volume = {2005},\n publisher = {in: Proc. IASTED International Conference on Education and Technology (ICET …},\n id = {ccc39b77-0cce-37cf-94fa-c92173d73f33},\n created = {2019-09-15T16:34:29.216Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-26T17:24:07.664Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {Videoconferencing technology offers new possibilities for distance education, as it provides an interactive way to teach remote students. To provide proper interactivity and to ensure the students' learning, it is important to transmit the correct view from the classroom to the remote sites. Traditionally the teacher has to take care of the source selection and control the equipment located in the classroom. From the teacher's point of view, this means additional work and concentrating solely on teaching can be difficult. The goal of the automatic system described in this paper is to reduce the teacher's workload in a videoconferencing situation. The system developed takes care of the video source switching without the teacher's control. The system observes the teacher's actions using the cameras installed in the classroom. A rule-based video source selection is made on the basis of both the teacher's location and the document camera usage information. Actual video source switching is carried out by the equipment and auditorium control unit installed in the classroom. The results obtained indicate that the system implemented can clearly provide help for the teacher when using a distance education system.},\n bibtype = {inproceedings},\n author = {Huttunen, Sami and Heikkilä, Janne and Silvén, Olli},\n booktitle = {Proceedings of the IASTED International Conference on Education and Technology, ICET 2005}\n}

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\n \n 2004\n \n \n (11)\n \n \n

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\n \n\n \n \n \n \n \n \n Pattern matching with affine moment descriptors.\n \n \n \n \n\n\n \n Heikkilä, J.\n\n\n \n\n\n\n Pattern Recognition, 37(9): 1825-1834. 9 2004.\n \n\n\n\n
\n\n\n\n \n \n $\"Pattern$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Pattern matching with affine moment descriptors},\n type = {article},\n year = {2004},\n keywords = {Affine moment invariants,Affine transformation,Registration},\n pages = {1825-1834},\n volume = {37},\n websites = {https://linkinghub.elsevier.com/retrieve/pii/S0031320304000974},\n month = {9},\n id = {a3897da2-2805-3338-bddb-aa44b8b44f90},\n created = {2019-09-15T16:34:26.167Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-10-18T15:33:56.506Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {JOUR},\n private_publication = {false},\n abstract = {This paper proposes a method for matching images based on their higher order moments without knowing the point correspondences. It is assumed that the disparity between the images can be explained by an affine transformation. The second-order statistics is used to transform the image points into canonical form, which reduces the affine matching problem for determining an orthonormal transformation matrix between the two point sets. Next, higher order complex moments are used to solve the remaining transformation. These affine moment descriptors are expressed in terms of the central moments of the original data. It is also shown that the resulting descriptors can be converted into affine moment invariants. A general framework for deriving affine moment descriptors as well as moment invariants is described. The experiments carried out with simulated data and real images indicate that the proposed method utilizing the second- and third-order statistics can provide good alignment results from noisy and spurious observations. © 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.},\n bibtype = {article},\n author = {Heikkilä, Janne},\n doi = {10.1016/j.patcog.2004.03.005},\n journal = {Pattern Recognition},\n number = {9}\n}

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\n \n\n \n \n \n \n \n A Framework for Proactive Machine Vision.\n \n \n \n\n\n \n Pietikäinen, M.; Silvén, O.; and Heikkilä, J.\n\n\n \n\n\n\n In Proc. Workshop on Processing Sensory Information for Proactive Systems (PSIPS 2004), 2004. in: Proc. Workshop on Processing Sensory Information for Proactive Systems …\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {A Framework for Proactive Machine Vision},\n type = {inproceedings},\n year = {2004},\n publisher = {in: Proc. Workshop on Processing Sensory Information for Proactive Systems …},\n id = {c6f3207c-89f4-3070-af97-bb572faca58c},\n created = {2019-09-15T16:34:26.570Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-10-18T15:33:56.827Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Pietikäinen, Matti and Silvén, Olli and Heikkilä, Janne},\n booktitle = {Proc. Workshop on Processing Sensory Information for Proactive Systems (PSIPS 2004)}\n}

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\n \n\n \n \n \n \n \n Human-computer interaction using head movements.\n \n \n \n\n\n \n Hannuksela, J.; Heikkilä, J.; and Pietikäinen, M.\n\n\n \n\n\n\n In Proc. Workshop on Processing Sensory Information for Proactive Systems (PSIPS 2004), pages 30-36, 2004. in: Proc. Workshop on Processing Sensory Information for Proactive Systems …\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Human-computer interaction using head movements},\n type = {inproceedings},\n year = {2004},\n pages = {30-36},\n publisher = {in: Proc. Workshop on Processing Sensory Information for Proactive Systems …},\n id = {bef198e4-3e3e-3df9-a987-2a57d337d74b},\n created = {2019-09-15T16:34:26.769Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-10-18T15:33:56.762Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Hannuksela, Jari and Heikkilä, Janne and Pietikäinen, Matti},\n booktitle = {Proc. Workshop on Processing Sensory Information for Proactive Systems (PSIPS 2004)}\n}

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\n \n\n \n \n \n \n \n A real-time facial feature based head tracker.\n \n \n \n\n\n \n Hannuksela, J.; Heikkila, J.; and Pietikainen, M.\n\n\n \n\n\n\n In Proc. Advanced Concepts for Intelligent Vision Systems, Brussels, Belgium, pages 267-272, 2004. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {A real-time facial feature based head tracker},\n type = {inproceedings},\n year = {2004},\n pages = {267-272},\n id = {30c9a2d2-a9c9-3742-8591-5ff10b59a399},\n created = {2019-09-15T16:34:26.850Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-10-18T15:33:56.969Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {JOUR},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Hannuksela, Jari and Heikkila, Janne and Pietikainen, M},\n booktitle = {Proc. Advanced Concepts for Intelligent Vision Systems, Brussels, Belgium}\n}

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\n \n\n \n \n \n \n \n \n Fast full search block motion estimation for H.264/AVC with multilevel successive elimination algorithm.\n \n \n \n \n\n\n \n Toivonen, T.; and Heikkilä, J.\n\n\n \n\n\n\n In 2004 International Conference on Image Processing, 2004. ICIP '04., volume 3, pages 1485-1488, 2004. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"Fast$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Fast full search block motion estimation for H.264/AVC with multilevel successive elimination algorithm},\n type = {inproceedings},\n year = {2004},\n pages = {1485-1488},\n volume = {3},\n websites = {http://ieeexplore.ieee.org/document/1421345/},\n publisher = {IEEE},\n id = {22b18fa2-227a-35db-83dd-49eac44a3c22},\n created = {2019-09-15T16:34:28.955Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-10-18T15:33:56.708Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {We modify the multilevel successive elimination algorithm (MSEA) to make it compatible with the motion estimation required for the H.264/AVC video coding standard. The algorithm must be changed to support the multiple block sizes and the criterion to be minimized has to be rate-distortion-based to allow efficient encoding. We use MSEA for eliminating the smallest 4×4-pixel blocks and either derive lower bound or exact criterion for larger blocks based on the small block bounds or criterions, correspondingly. The resulting algorithm needs 60%-70% less time than the original motion estimation in the reference encoder but will still yield practically equivalent video quality and bit rate. © 2004 IEEE.},\n bibtype = {inproceedings},\n author = {Toivonen, Tuukka and Heikkilä, Janne},\n doi = {10.1109/ICIP.2004.1421345},\n booktitle = {2004 International Conference on Image Processing, 2004. ICIP '04.}\n}

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\n \n\n \n \n \n \n \n \n Image scale and rotation from the phase-only bispectrum.\n \n \n \n \n\n\n \n Heikkila, J.\n\n\n \n\n\n\n In 2004 International Conference on Image Processing, 2004. ICIP '04., volume 3, pages 1783-1786, 2004. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"Image$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Image scale and rotation from the phase-only bispectrum},\n type = {inproceedings},\n year = {2004},\n pages = {1783-1786},\n volume = {3},\n websites = {http://ieeexplore.ieee.org/document/1421420/},\n publisher = {IEEE},\n id = {1c5f6d5b-8a22-3f03-9d1a-7790f3f478e1},\n created = {2019-09-15T16:34:28.972Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-10-18T15:33:56.842Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {This paper deals with the problem of aligning two images under translation, rotation and scaling. The method described utilizes the shift invariance property of the bispectrum to eliminate the effect of the translation component. Only the phase information is preserved from the bispectrum in order to achieve better resilience against nonuniform illumination changes. The scale and the rotation parameters are estimated from the remaining log-polar sampled spectrum using cross-correlation. The examples shown in the paper indicate that the method is quite robust against background clutter and occlusions. ©2004 IEEE.},\n bibtype = {inproceedings},\n author = {Heikkila, J.},\n doi = {10.1109/ICIP.2004.1421420},\n booktitle = {2004 International Conference on Image Processing, 2004. ICIP '04.}\n}

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\n \n\n \n \n \n \n \n \n Selection of the Lagrange multiplier for block-based motion estimation criteria.\n \n \n \n \n\n\n \n Sangi, P.; Heikkila, J.; and Silven, O.\n\n\n \n\n\n\n In 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing, volume 3, pages iii-325-8, 2004. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"Selection$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Selection of the Lagrange multiplier for block-based motion estimation criteria},\n type = {inproceedings},\n year = {2004},\n pages = {iii-325-8},\n volume = {3},\n websites = {http://ieeexplore.ieee.org/document/1326547/},\n publisher = {IEEE},\n id = {f1d9be08-d13e-38dd-9dd9-37a71c263c06},\n created = {2019-09-15T16:34:28.988Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-10-18T15:33:56.725Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {In hybrid video coding, motion vectors used for motion compensation constitute an important set of decisions. Cost functions for block motion estimation that take the smoothness of the resulting motion vector field into account, in addition to the motion compensated prediction error, have been proposed. Computationally simple derivatives of SAD and SSD-based criteria are studied in this paper. Cost functions are based on Lagrangian rate-distortion formulation, and the basic question is how the Lagrangian multiplier involved should be selected, Assumptions behind these cost functions are discussed, and a new method is derived for determining the multiplier. Comparisons with other strategies are made with experiments. The results show that the selection of the multiplier is not critical.},\n bibtype = {inproceedings},\n author = {Sangi, Pekka and Heikkila, J. and Silven, O.},\n doi = {10.1109/ICASSP.2004.1326547},\n booktitle = {2004 IEEE International Conference on Acoustics, Speech, and Signal Processing}\n}

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\n \n\n \n \n \n \n \n \n Object classification with multi-scale autoconvolution.\n \n \n \n \n\n\n \n Rahtu, E.; and Heikkila, J.\n\n\n \n\n\n\n In Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., volume 3, pages 37-40 Vol.3, 2004. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"Object$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Object classification with multi-scale autoconvolution},\n type = {inproceedings},\n year = {2004},\n pages = {37-40 Vol.3},\n volume = {3},\n websites = {http://ieeexplore.ieee.org/document/1334463/},\n publisher = {IEEE},\n id = {1e9f8cb9-df8a-3d2c-bc5c-4fe9aa4b52cf},\n created = {2019-09-15T16:34:29.062Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-10-18T15:33:56.619Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {This paper assesses the recently proposed affine invariant image transform called Multi-Scale Autoconvolution (MSA) in some practical object classification problems. A classification framework based on MSA and Support Vector Machines is introduced. As shown by the comparison with another affine invariant technique, it appears that this new technique provides a good basis for problems where the disturbances in classified objects can be approximated with spatial affine transformation. The paper also introduces a new property clarifying the parameter selection in the Multi-Scale Autoconvolution.},\n bibtype = {inproceedings},\n author = {Rahtu, Esa and Heikkila, J.},\n doi = {10.1109/ICPR.2004.1334463},\n booktitle = {Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.}\n}

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\n \n\n \n \n \n \n \n \n Motion analysis using frame differences with spatial gradient measures.\n \n \n \n \n\n\n \n Sangi, P.; Heikkila, J.; and Silven, O.\n\n\n \n\n\n\n In Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., volume 4, pages 733-736 Vol.4, 2004. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"Motion$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Motion analysis using frame differences with spatial gradient measures},\n type = {inproceedings},\n year = {2004},\n pages = {733-736 Vol.4},\n volume = {4},\n websites = {http://ieeexplore.ieee.org/document/1333877/},\n publisher = {IEEE},\n id = {a92aaf4c-67e2-3999-84b8-61648223bee7},\n created = {2019-09-15T16:34:29.070Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-10-18T15:33:56.638Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {The paper considers making inferences about the underlying true 2-D motion when only evaluations of a local block-based cost function, the mean of absolute or squared differences, for a set of motion candidates are available. Considering bounds for these criteria, it is shown that simple local image gradient measures provide useful information for interpreting the criterion values. Based on analysis, a thresholding scheme for the criteria is proposed. Using a Gaussian approximation for the thresholding result, estimates of local motions and related uncertainties can be obtained.},\n bibtype = {inproceedings},\n author = {Sangi, Pekka and Heikkila, J. and Silven, O.},\n doi = {10.1109/ICPR.2004.1333877},\n booktitle = {Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.}\n}

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\n \n\n \n \n \n \n \n \n Convexity recognition using multi-scale autoconvolution.\n \n \n \n \n\n\n \n Rahtu, E.; Salo, M.; and Heikkila, J.\n\n\n \n\n\n\n In Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., volume 1, pages 692-695 Vol.1, 2004. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"Convexity$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Convexity recognition using multi-scale autoconvolution},\n type = {inproceedings},\n year = {2004},\n pages = {692-695 Vol.1},\n volume = {1},\n websites = {http://ieeexplore.ieee.org/document/1334271/},\n publisher = {IEEE},\n id = {9775055a-3a70-33c3-a185-2ae485efe79e},\n created = {2019-09-15T16:34:29.119Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-10-18T15:33:56.877Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {This paper introduces a novel measure for object convexity using the recently introduced Multi-Scale Autoconvolution transform. The proposed measure is computationally efficient and recognizes even small errors in a convex domain. We also consider its implementation and give a complete Matlab algorithm for computing this measure for digital images. Finally, we give examples to verify its applicability and accuracy. The examples also consider convexity as a measure for complexity.},\n bibtype = {inproceedings},\n author = {Rahtu, Esa and Salo, Mikko and Heikkila, J.},\n doi = {10.1109/ICPR.2004.1334271},\n booktitle = {Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.}\n}

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\n \n\n \n \n \n \n \n A new class of shift-invariant operators.\n \n \n \n\n\n \n Heikkila, J.\n\n\n \n\n\n\n IEEE signal processing letters, 11(6): 545-548. 2004.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{\n title = {A new class of shift-invariant operators},\n type = {article},\n year = {2004},\n pages = {545-548},\n volume = {11},\n id = {8a99cf8c-d822-3697-84a1-121af150424f},\n created = {2019-09-15T16:34:29.175Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-10-18T15:33:56.462Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {JOUR},\n private_publication = {false},\n bibtype = {article},\n author = {Heikkila, Janne},\n journal = {IEEE signal processing letters},\n number = {6}\n}

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\n \n 2003\n \n \n (3)\n \n \n

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\n \n\n \n \n \n \n \n Efficient method for half-pixel block motion estimation using block differentials.\n \n \n \n\n\n \n Toivonen, T.; and Heikkilä, J.\n\n\n \n\n\n\n In Visual Content Processing and Representation. VLBV 2003. Lecture Notes in Computer Science, volume 2849, pages 225-232, 2003. Springer, Berlin, Heidelberg\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Efficient method for half-pixel block motion estimation using block differentials},\n type = {inproceedings},\n year = {2003},\n pages = {225-232},\n volume = {2849},\n publisher = {Springer, Berlin, Heidelberg},\n id = {31536421-c854-3012-a48e-78c12bda05da},\n created = {2019-09-15T16:34:27.299Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-15T18:07:37.614Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Toivonen, Tuukka and Heikkilä, Janne},\n booktitle = {Visual Content Processing and Representation. VLBV 2003. Lecture Notes in Computer Science}\n}

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\n \n\n \n \n \n \n \n \n A statistical method for object alignment under affine transformation.\n \n \n \n \n\n\n \n Heikkila, J.\n\n\n \n\n\n\n In 12th International Conference on Image Analysis and Processing, 2003.Proceedings., pages 360-365, 2003. IEEE Comput. Soc\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {A statistical method for object alignment under affine transformation},\n type = {inproceedings},\n year = {2003},\n pages = {360-365},\n websites = {http://ieeexplore.ieee.org/document/1234076/},\n publisher = {IEEE Comput. Soc},\n id = {b58823cd-04f7-3f03-a91a-59b16c2926b1},\n created = {2019-09-15T16:34:28.617Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-10-18T15:33:56.599Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {The paper presents a novel approach for aligning a pair of sparse point sets under the assumption that their disparity is mainly explained by an affine transformation. The basic idea is to decompose the affine transformation matrix into a product of three matrices that can be estimated separately. Two matrices are obtained using Cholesky factorization of the sample covariance matrices, and the remaining matrix using the third order central moments of the point sets. The method is computationally efficient, and the experimental results with real images indicate that the proposed approach can give a good accuracy for alignment. However, only a small amount of clutter can be tolerated. In a general situation, it necessary to apply some preprocessing to segment the objects before applying the algorithm. © 2003 IEEE.},\n bibtype = {inproceedings},\n author = {Heikkila, J.},\n doi = {10.1109/ICIAP.2003.1234076},\n booktitle = {12th International Conference on Image Analysis and Processing, 2003.Proceedings.}\n}

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\n \n\n \n \n \n \n \n \n A new rate-minimizing matching criterion and a fast algorithm for block motion estimation.\n \n \n \n \n\n\n \n Toivonen, T.; and Heikkilä, J.\n\n\n \n\n\n\n In Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), volume 3, pages II-355-8, 2003. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {A new rate-minimizing matching criterion and a fast algorithm for block motion estimation},\n type = {inproceedings},\n year = {2003},\n pages = {II-355-8},\n volume = {3},\n websites = {http://ieeexplore.ieee.org/document/1246690/},\n publisher = {IEEE},\n id = {62b43f81-5088-34c9-9ba9-e211df986422},\n created = {2019-09-15T16:34:28.929Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-10-18T15:33:56.464Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {We present a new block matching criterion for motion estimation in video coding that will give better encoded video quality than the commonly used sum of absolute differences (SAD) or even sum of squared differences (SSD) criteria. The new criterion tends to concentrate the discrete cosine transformed block energy into DC frequency which may allow coding the AC coefficients with less bits. The criterion gives best results on sequences which have varying lighting conditions. Furthermore, we modify the Successive Elimination Algorithm (SEA) and Multilevel Successive Elimination Algorithm (MSEA) to be usable with the new criterion by deriving a new tighter lower bound for the SSD criterion. The new bound can be used either directly with the SSD criterion or with the new bit-rate minimizing criterion.},\n bibtype = {inproceedings},\n author = {Toivonen, Tuukka and Heikkilä, Janne},\n doi = {10.1109/ICIP.2003.1246690},\n booktitle = {Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429)}\n}

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\n \n 2002\n \n \n (4)\n \n \n

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\n \n\n \n \n \n \n \n \n Multi-scale auto-convolution for affine invariant pattern recognition.\n \n \n \n \n\n\n \n Heikkilä, J.\n\n\n \n\n\n\n In Proc. International Conference on Pattern Recognition, volume 1, pages 119-122, 2002. IEEE Comput. Soc\n \n\n\n\n
\n\n\n\n \n \n $\"Multi-scale$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Multi-scale auto-convolution for affine invariant pattern recognition},\n type = {inproceedings},\n year = {2002},\n pages = {119-122},\n volume = {1},\n websites = {http://ieeexplore.ieee.org/document/1044627/},\n publisher = {IEEE Comput. Soc},\n id = {b4632a93-2c30-3e4b-bf59-3ffe85d3c330},\n created = {2019-09-15T16:34:26.089Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-10-18T15:33:57.022Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Heikkilä, Janne},\n doi = {10.1109/ICPR.2002.1044627},\n booktitle = {Proc. International Conference on Pattern Recognition}\n}

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\n \n\n \n \n \n \n \n \n Moment and curvature preserving technique for accurate ellipse boundary detection.\n \n \n \n \n\n\n \n Heikkilä, J.\n\n\n \n\n\n\n In Proceedings. Fourteenth International Conference on Pattern Recognition, volume 1, pages 734-737, 2002. IEEE Comput. Soc\n \n\n\n\n
\n\n\n\n \n \n $\"Moment$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Moment and curvature preserving technique for accurate ellipse boundary detection},\n type = {inproceedings},\n year = {2002},\n pages = {734-737},\n volume = {1},\n websites = {http://ieeexplore.ieee.org/document/711250/},\n publisher = {IEEE Comput. Soc},\n id = {81d4b519-ffd3-3491-82f6-440b33d60719},\n created = {2019-09-15T16:34:26.163Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-10-18T15:33:56.878Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {Circles and their elliptic projections are very commonly used\\nimage features in computer vision applications. Thus, it is very\\nimportant to be able to determine their location in an accurate manner.\\nA technique for determining an ellipse boundary with subpixel precision\\nis proposed. The technique, called the moment and curvature preserving\\ndetection (MCP), utilizes the first three intensity moments and the\\nintensity gradient of the image. The idea of using moments for subpixel\\nedge detection is not new, but in the case of ellipses the moments do\\nnot provide sufficient information for precise detection. However, if\\nthe local curvature is augmented to the observations, the ellipse\\nboundary can be determined reliably},\n bibtype = {inproceedings},\n author = {Heikkilä, Janne},\n doi = {10.1109/ICPR.1998.711250},\n booktitle = {Proceedings. Fourteenth International Conference on Pattern Recognition}\n}

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\n \n\n \n \n \n \n \n \n A new algorithm for fast full search block motion estimation based on number theoretic transforms.\n \n \n \n \n\n\n \n Toivonen, T.; Heikkilä, J.; and Silven, O.\n\n\n \n\n\n\n In Recent Trends in Multimedia Information Processing, pages 90-94, 10 2002. WORLD SCIENTIFIC\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {A new algorithm for fast full search block motion estimation based on number theoretic transforms},\n type = {inproceedings},\n year = {2002},\n pages = {90-94},\n websites = {http://www.worldscientific.com/doi/abs/10.1142/9789812776266_0012},\n month = {10},\n publisher = {WORLD SCIENTIFIC},\n id = {1bb1ba57-0b06-3db3-8d44-a25c52dd9f7b},\n created = {2019-09-15T16:34:27.099Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-10-18T15:33:56.741Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CHAP},\n private_publication = {false},\n abstract = {A new fast full search algorithm for block motion estimation is presented, which is based on convolution theorem and number theoretic transforms. It can be used with common video coding standards such as H.263 and MPEG. The algorithm ap-plies the sum of squared differences (SSD) criterion, and the encoded video quality is equivalent or even better than what is achieved with conventional methods, but the algorithm has low theoretical complexity. The algorithm is implemented for H.263 software video encoder, and a great reduction in execution time is achieved.},\n bibtype = {inproceedings},\n author = {Toivonen, Tuukka and Heikkilä, Janne and Silven, Olli},\n doi = {10.1142/9789812776266_0012},\n booktitle = {Recent Trends in Multimedia Information Processing}\n}

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\n \n\n \n \n \n \n \n \n Linear motion estimation for image sequence based accurate 3-D measurements.\n \n \n \n \n\n\n \n Heikkilä, J.; and Silven, O.\n\n\n \n\n\n\n In Proceedings. Fourteenth International Conference on Pattern Recognition, volume 2, pages 1247-1250, 2002. IEEE Comput. Soc\n \n\n\n\n
\n\n\n\n \n \n $\"Linear$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Linear motion estimation for image sequence based accurate 3-D measurements},\n type = {inproceedings},\n year = {2002},\n pages = {1247-1250},\n volume = {2},\n websites = {http://ieeexplore.ieee.org/document/711926/},\n publisher = {IEEE Comput. Soc},\n id = {6a5a308a-36ce-3a3d-9861-6729f82a2118},\n created = {2019-09-15T16:34:28.733Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-10-18T15:33:56.366Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {We present a method for making accurate 3-D measurements from monocular image sequences. The process of determining camera motion is completely separated from 3-D structure estimation. The algorithm has two steps: elimination of rotations and estimation of the camera translation. Elimination of rotations is based on pre-calibration, and estimation of the camera translation is based on locating the focus of expansion from image disparities. The method proposed utilizes the total least squares estimation technique. By using the motion data, the 3-D coordinates of the measurement points can be solved linearly up to a scale factor. Due to the nonrecursive nature of the method, it provides a fast approach for processing long image sequences in an accurate manner},\n bibtype = {inproceedings},\n author = {Heikkilä, Janne and Silven, Olli},\n doi = {10.1109/ICPR.1998.711926},\n booktitle = {Proceedings. Fourteenth International Conference on Pattern Recognition}\n}

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\n \n 2001\n \n \n (4)\n \n \n

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\n \n\n \n \n \n \n \n A Multi-view Camera Calibration Method for Coplanar Targets.\n \n \n \n\n\n \n Heikkilä, J.\n\n\n \n\n\n\n In Proc. Scandinavian Conference on Image Analysis, pages 409-414, 2001. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {A Multi-view Camera Calibration Method for Coplanar Targets},\n type = {inproceedings},\n year = {2001},\n pages = {409-414},\n id = {878e2093-109b-3bb3-bc3c-ebb2cffd48b4},\n created = {2019-09-15T16:34:26.649Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-10-18T15:33:56.753Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Heikkilä, Janne},\n booktitle = {Proc. Scandinavian Conference on Image Analysis}\n}

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\n \n\n \n \n \n \n \n Experiments in 3D measurements by using single camera and accurate motion.\n \n \n \n\n\n \n Heimonen, T.; Hannuksela, J.; Heikkilä, J.; Leinonen, J.; and Manninen, M.\n\n\n \n\n\n\n In Proceedings of the IEEE International Symposium on Assembly and Task Planning, pages 356-361, 2001. IEEE\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Experiments in 3D measurements by using single camera and accurate motion},\n type = {inproceedings},\n year = {2001},\n pages = {356-361},\n publisher = {IEEE},\n id = {757ef10a-c9c1-3b3b-b2f8-8265ce043cd3},\n created = {2019-09-15T16:34:28.784Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-10-18T15:33:56.600Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {A method to capture 3D coordinates of object features is described and preliminary results of our experiments are presented. Our method is based on the utilisation of traditional parallax equations, determination of undistorted image coordinates, and accurately known motion of a CCD-camera. Error sources of the method are described and equations to estimate the effect of these errors on the results of the 3D reconstruction derived. An experimental setup suitable for light assembly application measurements was constructed. The properties of our method were analysed and preliminary 3D reconstruction experiments were performed with the aid of the setup facilities. A relative accuracy 1:6000 (0.016 mm RMSE) was obtained, when a test object was measured.},\n bibtype = {inproceedings},\n author = {Heimonen, T and Hannuksela, J and Heikkilä, Janne and Leinonen, J and Manninen, M},\n doi = {10.1109/isatp.2001.929051},\n booktitle = {Proceedings of the IEEE International Symposium on Assembly and Task Planning}\n}

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\n \n\n \n \n \n \n \n Extracting motion components from image sequences using particle filters.\n \n \n \n\n\n \n Sangi, P.; Heikkilä, J.; and Silvén, O.\n\n\n \n\n\n\n In Proceedings of the scandinavian conference on image analysis, pages 508-514, 2001. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Extracting motion components from image sequences using particle filters},\n type = {inproceedings},\n year = {2001},\n pages = {508-514},\n id = {610bc344-ad08-31cf-a654-13558bd0ff39},\n created = {2019-09-15T16:34:28.893Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-10-18T15:33:56.586Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Sangi, Pekka and Heikkilä, Janne and Silvén, Olli},\n booktitle = {Proceedings of the scandinavian conference on image analysis}\n}

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\n \n\n \n \n \n \n \n \n The relationship of handedness to asymmetry in the occlusal morphology of first permanent molars.\n \n \n \n \n\n\n \n Pirilä-Parkkinen, K.; Pirttiniemi, P.; Alvesalo, L.; Silvén, O.; Heikkilä, J.; and Osborne, R., H.\n\n\n \n\n\n\n European Journal of Morphology, 39(2): 81-89. 4 2001.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {The relationship of handedness to asymmetry in the occlusal morphology of first permanent molars},\n type = {article},\n year = {2001},\n keywords = {Laterality,Odontometry},\n pages = {81-89},\n volume = {39},\n websites = {http://access.portico.org/stable?au=pggtrmzzpg},\n month = {4},\n day = {1},\n id = {becc2c35-c3cf-3ae0-838e-9e683a65d564},\n created = {2019-10-18T15:33:56.143Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-10-18T15:33:56.143Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {JOUR},\n private_publication = {false},\n abstract = {Handedness has been shown to be related to a number of systematic asymmetries in body dimensions, dermatoglyphic patterns and cerebral morphology. The aim here was to compare linear and angular tooth crown asymmetries of the permanent molars in healthy right-handed and left-handed subjects. The material comprised 27 children with recorded concordant left-side dominance of hand, eye and foot. The controls were an age- and sex-matched group with right side dominance. The material is based on the Collaborative Perinatal Project where detailed medical records and the dentitions, including accurate dental impressions, of over two thousand American children were examined in the USA in the sixties. Machine vision technique was used to obtain accurate three-dimensional information from the occlusal surfaces of the first permanent upper and lower molars. The directional asymmetry values of angular measurements of mandibular first molars showed evidence of asymmetry of opposite direction between the two examined groups. The results indicate that occlusal morphology of first permanent molars may be affected by handedness, and this tendency is most evident in the angular measurements of the mandibular molars. Fluctuating asymmetry did not differ significantly between the examined groups.},\n bibtype = {article},\n author = {Pirilä-Parkkinen, Kirsi and Pirttiniemi, Pertti and Alvesalo, Lassi and Silvén, Olli and Heikkilä, Janne and Osborne, Richard H},\n doi = {10.1076/ejom.39.2.81.7367},\n journal = {European Journal of Morphology},\n number = {2}\n}

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\n \n 2000\n \n \n (4)\n \n \n

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\n \n\n \n \n \n \n \n \n Geometric camera calibration using circular control points.\n \n \n \n \n\n\n \n Heikkila, J.\n\n\n \n\n\n\n IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(10): 1066-1077. 2000.\n \n\n\n\n
\n\n\n\n \n \n $\"Geometric$ Paper\n \n \n \n $\"Geometric$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{\n title = {Geometric camera calibration using circular control points},\n type = {article},\n year = {2000},\n pages = {1066-1077},\n volume = {22},\n websites = {http://ieeexplore.ieee.org/document/879788/},\n id = {8d43bb9b-9f0d-3818-8432-fa185bbeb306},\n created = {2019-09-15T16:34:26.289Z},\n file_attached = {true},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2020-06-05T09:51:57.455Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {JOUR},\n private_publication = {false},\n abstract = {Modern CCD cameras are usually capable of a spatial accuracy greater than 1/50 of the pixel size. However, such accuracy is not easily attained due to various error sources that can affect the image formation process. Current calibration methods typically assume that the observations are unbiased, the only error is the zero-mean independent and identically distributed random noise in the observed image coordinates, and the camera model completely explains the mapping between the 3-D coordinates and the image coordinates. In general, these conditions are not met, causing the calibration results to be less accurate than expected. In this paper, a calibration procedure for precise 3-D computer vision applications is described. It introduces bias correction for circular control points and a non-recursive method for reversing the distortion model. The accuracy analysis is presented and the error sources that can reduce the theoretical accuracy are discussed. The tests with synthetic images indicate improvements in the calibration results in limited error conditions. In real images, the suppression of external error sources becomes a prerequisite for successful calibration.},\n bibtype = {article},\n author = {Heikkila, J.},\n doi = {10.1109/34.879788},\n journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},\n number = {10}\n}

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\n \n\n \n \n \n \n \n A real-time tracker for visual surveillance applications.\n \n \n \n\n\n \n Heikkilä, J.\n\n\n \n\n\n\n In Proceedings 1st Int. Workshop on PETS, 2000, 2000. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {A real-time tracker for visual surveillance applications},\n type = {inproceedings},\n year = {2000},\n id = {a05ac731-fdf2-3f52-8c96-bfa6990360ea},\n created = {2019-09-15T16:34:27.229Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-10-18T15:33:56.596Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Heikkilä, Janne},\n booktitle = {Proceedings 1st Int. Workshop on PETS, 2000}\n}

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\n \n\n \n \n \n \n \n \n Camera motion estimation from non-stationary scenes using EM-based motion segmentation.\n \n \n \n \n\n\n \n Heikkila, J.; Sangi, P.; and Silven, O.\n\n\n \n\n\n\n In Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, volume 1, pages 370-374, 2000. IEEE Comput. Soc\n \n\n\n\n
\n\n\n\n \n \n $\"Camera$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Camera motion estimation from non-stationary scenes using EM-based motion segmentation},\n type = {inproceedings},\n year = {2000},\n pages = {370-374},\n volume = {1},\n issue = {1},\n websites = {http://ieeexplore.ieee.org/document/905355/},\n publisher = {IEEE Comput. Soc},\n id = {def1db66-38dd-34a3-8d1a-8d2996549020},\n created = {2019-09-15T16:34:28.691Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-10-18T15:33:56.501Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {A new algorithm for recovering 3-D camera motion from sequences of images is proposed. The algorithm has four stages. In the first stage, the motion vector field is segmented using a novel EM-based method. The resulting segments are compared and the coherent regions are merged in the second stage. The candidates for the background regions are determined and finally used for 3-D motion estimation in the last two stages. Unlike most of the other methods, this approach tolerates also non-rigid motion in the scene. The experiments performed show that in some cases more information or reasoning is needed for selecting plausible motion parameters from several hypotheses. ©2000 IEEE.},\n bibtype = {inproceedings},\n author = {Heikkila, J. and Sangi, Pekka and Silven, O.},\n doi = {10.1109/ICPR.2000.905355},\n booktitle = {Proceedings 15th International Conference on Pattern Recognition. ICPR-2000}\n}

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\n \n\n \n \n \n \n \n \n Intensity independent color models and visual tracking.\n \n \n \n \n\n\n \n Korhonen, M.; Heikkilä, J.; and Silvén, O.\n\n\n \n\n\n\n Proceedings - International Conference on Pattern Recognition, 15(3): 600-604. 2000.\n \n\n\n\n
\n\n\n\n \n \n $\"Intensity$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{\n title = {Intensity independent color models and visual tracking},\n type = {article},\n year = {2000},\n pages = {600-604},\n volume = {15},\n websites = {http://ieeexplore.ieee.org/document/903617/},\n publisher = {IEEE Comput. Soc},\n id = {7a777531-535f-30aa-a7b1-e0017670095a},\n created = {2019-09-15T16:34:28.848Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-10-18T15:33:56.470Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {Some intensity independent color models are studied experimentally in the scope of visual tracking to introduce robustness to illumination changes. Also, a plain color background model to allow modest camera motion is presented. The background is represented as a Gaussian mixture in color space. The EM algorithm is applied to find the decomposition and the Minimum Description Length principle is proposed to determine the number of mixture components. A simple tracking algorithm is outlined and the achieved results are introduced. © 2000 IEEE.},\n bibtype = {article},\n author = {Korhonen, Mika and Heikkilä, Janne and Silvén, Olli},\n doi = {10.1109/icpr.2000.903617},\n journal = {Proceedings - International Conference on Pattern Recognition},\n number = {3}\n}

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\n \n 1999\n \n \n (2)\n \n \n

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\n \n\n \n \n \n \n \n \n A real-time system for monitoring of cyclists and pedestrians.\n \n \n \n \n\n\n \n Heikkilä, J.; and Silven, O.\n\n\n \n\n\n\n In Proceedings Second IEEE Workshop on Visual Surveillance (VS'99), pages 74-81, 1999. IEEE Comput. Soc\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {A real-time system for monitoring of cyclists and pedestrians},\n type = {inproceedings},\n year = {1999},\n pages = {74-81},\n websites = {http://ieeexplore.ieee.org/document/780271/},\n publisher = {IEEE Comput. Soc},\n id = {4e414888-613b-34a6-b455-e5895547260c},\n created = {2019-09-15T16:34:26.198Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-10-18T15:33:56.866Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {Camera based fixed systems are routinely used for monitoring highway traffic. For this purpose inductive loops and microwave sensors are mainly used. Both techniques achieve very good counting accuracy and are capable of discriminating trucks and cars. However pedestrians and cyclists are mostly counted manually. In this paper, we describe a new camera based automatic system that utilizes Kalman filtering in tracking and Learning Vector Quantization (LVQ) for classifying the observations to pedestrians and cyclists. Both the requirements for such systems and the algorithms used are described. The tests performed show that the system achieves around 80%-90% accuracy in counting and classification.},\n bibtype = {inproceedings},\n author = {Heikkilä, Janne and Silven, Olli},\n doi = {10.1109/VS.1999.780271},\n booktitle = {Proceedings Second IEEE Workshop on Visual Surveillance (VS'99)}\n}

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\n \n\n \n \n \n \n \n Experiments with shape-based deformable object tracking.\n \n \n \n\n\n \n Sangi, P.; Heikkilä, J.; and Silven, O.\n\n\n \n\n\n\n In Proc. Scandinavian Conference on Image Analysis, volume 1, pages 311-318, 1999. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Experiments with shape-based deformable object tracking},\n type = {inproceedings},\n year = {1999},\n pages = {311-318},\n volume = {1},\n id = {ed1c8396-f7f8-3e92-90bd-341ace625832},\n created = {2019-09-15T16:34:26.892Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-10-18T15:33:56.855Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Sangi, P and Heikkilä, J and Silven, O},\n booktitle = {Proc. Scandinavian Conference on Image Analysis}\n}

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\n \n 1998\n \n \n (1)\n \n \n

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\n \n\n \n \n \n \n \n Asymmetry in the occlusal morphology of first permanent molars in 45,X/46,XX mosaics.\n \n \n \n\n\n \n Pirttiniemi, P.; Alvesalo, L.; Silvén, O.; Heikkilä, J.; Julku, J.; and Karjalahti, P.\n\n\n \n\n\n\n Archives of Oral Biology, 43(1): 25-32. 1998.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Asymmetry in the occlusal morphology of first permanent molars in 45,X/46,XX mosaics},\n type = {article},\n year = {1998},\n keywords = {Molar,Mosaicism,Odontometry,Sex chromosome abnormalities},\n pages = {25-32},\n volume = {43},\n id = {4e0fa2fa-526b-38ba-a91c-1709d8f647fe},\n created = {2019-09-15T16:34:27.370Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-15T18:07:37.947Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {JOUR},\n private_publication = {false},\n abstract = {The genetic control of dental morphology is affected by various chromosomal aberrations, and morphological changes familiar to specific aneuploidies can be distinguished in many cases. Asymmetry between bilateral teeth in the dental arch in laboratory animals shows increased expression after exposure to external stress during development. Bilateral asymmetry in occlusal cuspal morphology has not been widely used as a means of odontometric examination, partly because accurate and reliable methods are not commonly available. The aim here was to examine linear and angular variables of the occlusal morphology of maxillary and mandibular first permanent molars in three dimensions in individuals with 45,X/46,XX mosaicism and to find out if this aneuploidism causes deviations from normal development and increased asymmetry in bilateral variables of the occlusal surface. The participants were five females with 45,X/46,XX chromosome constitution, whose karyotypes were confirmed by cytogenetic tests of skin fibroblasts. The controls were 10 first-degree female relatives of the mosaic patients with normal 46,XX chromosome constitution. The method of measuring the three-dimensional morphology of occlusal surfaces was based on a machine- vision technique using a single video-imaging camera. An apparent increase in asymmetry of occlusal morphology in first permanent molars in 45,X/46,XX mosaics was found. As there was evidence of directional asymmetry, it is possible that different cell lines regulated by discrete genes cause the directionality.},\n bibtype = {article},\n author = {Pirttiniemi, Pertti and Alvesalo, Lassi and Silvén, O. and Heikkilä, Janne and Julku, Johanna and Karjalahti, P},\n doi = {10.1016/S0003-9969(97)00094-0},\n journal = {Archives of Oral Biology},\n number = {1}\n}

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\n \n 1997\n \n \n (3)\n \n \n

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\n \n\n \n \n \n \n \n Camera calibration and image correction using circular control points.\n \n \n \n\n\n \n Heikkilä, J.; and Silven, O.\n\n\n \n\n\n\n In Scandinavian conference on image analysis, pages 847-854, 1997. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Camera calibration and image correction using circular control points},\n type = {inproceedings},\n year = {1997},\n pages = {847-854},\n id = {153e98da-ef47-3bef-8b06-27f60142ddb0},\n created = {2019-09-15T16:34:27.092Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-10-18T15:33:56.462Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Heikkilä, J and Silven, O},\n booktitle = {Scandinavian conference on image analysis}\n}

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\n \n\n \n \n \n \n \n \n Accurate camera calibration and feature based 3-D reconstruction from monocular image sequences.\n \n \n \n \n\n\n \n Heikkilä, J.\n\n\n \n\n\n\n Ph.D. Thesis, 1997.\n \n\n\n\n
\n\n\n\n \n \n $\"Accurate$ Website\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@phdthesis{\n title = {Accurate camera calibration and feature based 3-D reconstruction from monocular image sequences},\n type = {phdthesis},\n year = {1997},\n source = {Acta Universitatis Ouluensis},\n websites = {http://www.ee.oulu.fi/mvg/page/publications/ID/160},\n institution = {University of Oulu},\n id = {e710a891-fb2c-3760-bfcf-1d3ea5687ce0},\n created = {2019-09-15T16:34:27.478Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-15T18:07:37.779Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {Doctoral Dissertation},\n private_publication = {false},\n abstract = {In this thesis, computational methods are developed for measuring three-dimensional structure from image sequences. The measurement process contains several stages, in which the intensity information obtained from a moving video camera is transformed into three-dimensional spatial coordinates. The proposed approach utilizes either line or circular features, which are automatically observed from each camera position. The two-dimensional data gathered from a sequence of digital images is then integrated into a three-dimensional model. This process is divided into three major computational issues: data acquisition, geometric camera calibration, and 3-D structure estimation. The purpose of data acquisition is to accurately locate the features from individual images. This task is performed by first determining the intensity boundary of each feature with subpixel precision, and then fitting a geometric model of the expected feature type into the boundary curve. The resulting parameters fully describe the two-dimensional location of the feature with respect to the image coordinate system. The feature coordinates obtained can be used as input data both in camera calibration and 3-D structure estimation. Geometric camera calibration is required for correcting the spatial errors in the images. Due to various error sources video cameras do not typically produce a perfect perspective projection. The feature coordinates determined are therefore systematically distorted. In order to correct the distortion, both a comprehensive camera model and a procedure for computing the model parameters are required. The calibration procedure proposed in this thesis utilizes circular features in the computation of the camera parameters. A new method for correcting the image coordinates is also presented. Estimation of the 3-D scene structure from image sequences requires the camera position and orientation to be known for each image. Thus, camera motion estimation is closely related to the 3- D structure estimation, and generally, these two tasks must be performed in parallel causing the estimation problem to be nonlinear. However, if the motion is purely translational, or the rotation component is known in advance, the motion estimation process can be separated from 3-D structure estimation. As a consequence, linear techniques for accurately computing both camera motion and 3- D coordinates of the features can be used. A major advantage of using an image sequence based measurement technique is that the correspondence problem of traditional stereo vision is mainly avoided. The image sequence can be captured with short inter-frame steps causing the disparity between successive images to be so small that the correspondences can be easily determined with a simple tracking technique. Furthermore, if the motion is translational, the shapes of the features are only slightly deformed during the sequence.},\n bibtype = {phdthesis},\n author = {Heikkilä, Janne}\n}

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\n\n\n

\n In this thesis, computational methods are developed for measuring three-dimensional structure from image sequences. The measurement process contains several stages, in which the intensity information obtained from a moving video camera is transformed into three-dimensional spatial coordinates. The proposed approach utilizes either line or circular features, which are automatically observed from each camera position. The two-dimensional data gathered from a sequence of digital images is then integrated into a three-dimensional model. This process is divided into three major computational issues: data acquisition, geometric camera calibration, and 3-D structure estimation. The purpose of data acquisition is to accurately locate the features from individual images. This task is performed by first determining the intensity boundary of each feature with subpixel precision, and then fitting a geometric model of the expected feature type into the boundary curve. The resulting parameters fully describe the two-dimensional location of the feature with respect to the image coordinate system. The feature coordinates obtained can be used as input data both in camera calibration and 3-D structure estimation. Geometric camera calibration is required for correcting the spatial errors in the images. Due to various error sources video cameras do not typically produce a perfect perspective projection. The feature coordinates determined are therefore systematically distorted. In order to correct the distortion, both a comprehensive camera model and a procedure for computing the model parameters are required. The calibration procedure proposed in this thesis utilizes circular features in the computation of the camera parameters. A new method for correcting the image coordinates is also presented. Estimation of the 3-D scene structure from image sequences requires the camera position and orientation to be known for each image. Thus, camera motion estimation is closely related to the 3- D structure estimation, and generally, these two tasks must be performed in parallel causing the estimation problem to be nonlinear. However, if the motion is purely translational, or the rotation component is known in advance, the motion estimation process can be separated from 3-D structure estimation. As a consequence, linear techniques for accurately computing both camera motion and 3- D coordinates of the features can be used. A major advantage of using an image sequence based measurement technique is that the correspondence problem of traditional stereo vision is mainly avoided. The image sequence can be captured with short inter-frame steps causing the disparity between successive images to be so small that the correspondences can be easily determined with a simple tracking technique. Furthermore, if the motion is translational, the shapes of the features are only slightly deformed during the sequence.\n

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\n \n\n \n \n \n \n \n \n A four-step camera calibration procedure with implicit image correction.\n \n \n \n \n\n\n \n Heikkilä, J.; and Silven, O.\n\n\n \n\n\n\n In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, volume 97, pages 1106-1112, 1997. IEEE Comput. Soc\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {A four-step camera calibration procedure with implicit image correction},\n type = {inproceedings},\n year = {1997},\n pages = {1106-1112},\n volume = {97},\n websites = {http://ieeexplore.ieee.org/document/609468/},\n publisher = {IEEE Comput. Soc},\n id = {b448a833-2b9d-3101-b22b-5ded0aa179fb},\n created = {2019-10-18T15:33:56.145Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2020-09-23T11:35:22.793Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n folder_uuids = {67b5fbfc-903a-4f35-8d95-f58fc1430bfd,b37b847e-2698-4bae-ad96-1473e506c76b},\n private_publication = {false},\n abstract = {In geometrical camera calibration the objective is to determine a set of camera parameters that describe the mapping between 3-D reference coordinates and 2-D image coordinates. Various methods for camera calibration can be found from the literature. However, surprisingly little attention has been paid to the whole calibration procedure, i.e., control point extraction from images, model fitting, image correction, and errors originating in these stages. The main interest has been in model fitting, although the other stages are also important. In this paper we present a four-step calibration procedure that is an extension to the two-step method. There is an additional step to compensate for distortion caused by circular features, and a step for correcting the distorted image coordinates. The image correction is performed with an empirical inverse model that accurately compensates for radial and tangential distortions. Finally, a linear method for solving the parameters of the inverse model is presented.},\n bibtype = {inproceedings},\n author = {Heikkilä, Janne and Silven, Olli},\n doi = {10.1109/CVPR.1997.609468},\n booktitle = {Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition}\n}

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\n \n 1996\n \n \n (2)\n \n \n

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\n \n\n \n \n \n \n \n \n Calibration procedure for short focal length off-the-shelf CCD cameras.\n \n \n \n \n\n\n \n Heikkilä, J.; and Silven, O.\n\n\n \n\n\n\n In Proceedings of 13th International Conference on Pattern Recognition, volume 1, pages 166-170 vol.1, 1996. IEEE\n \n\n\n\n
\n\n\n\n \n \n $\"Calibration$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {Calibration procedure for short focal length off-the-shelf CCD cameras},\n type = {inproceedings},\n year = {1996},\n pages = {166-170 vol.1},\n volume = {1},\n websites = {http://ieeexplore.ieee.org/document/546012/},\n publisher = {IEEE},\n id = {a1eb5148-a58c-3d50-8d7d-999a4debabf7},\n created = {2019-09-15T16:34:26.203Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-10-18T15:33:56.956Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {A camera calibration procedure intended for a 3D measurement application is presented, paying attention to the various error sources. The error may be measurement noise that is random by nature, but it may also be systematic originating from the calibration target used, geometrical distortions and illumination. In order to obtain good calibration results, the systematic error sources should be eliminated or their effects compensated for. Then, the camera parameters can be determined by fitting the corrected measurements to the camera model which in our case is a combination of a pinhole camera and lens distortion models. We also notice that a more complete camera model is needed to explain all the error components. © 1996 IEEE.},\n bibtype = {inproceedings},\n author = {Heikkilä, Janne and Silven, O.},\n doi = {10.1109/ICPR.1996.546012},\n booktitle = {Proceedings of 13th International Conference on Pattern Recognition}\n}

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\n \n\n \n \n \n \n \n Accurate 3-D measurement using a single video camera.\n \n \n \n\n\n \n Heikkilä, J.; and Silvén, O.\n\n\n \n\n\n\n International Journal of Pattern Recognition and Artificial Intelligence, 10(2): 139-149. 1996.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Accurate 3-D measurement using a single video camera},\n type = {article},\n year = {1996},\n keywords = {Camera calibration,Coordinate measurement,Extended Kalman filtering,Image sequences,Visual tracking},\n pages = {139-149},\n volume = {10},\n id = {8606f8ad-d9ce-3b26-9edb-d6895d5c9088},\n created = {2019-09-15T16:34:27.421Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-09-15T18:07:37.785Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {JOUR},\n private_publication = {false},\n abstract = {We present a straightforward technique for determining the 3-D locations of feature points using sequences of monocular image frames captured by a moving camera. The motion of the camera is estimated simultaneously. In practice, only the camera needs careful calibration. Based on experiments, the repeatability is currently about 1/3500 and accuracy 1/2500. This approach has potential for high speed, as hundreds of points may be measured from the same image sequence.},\n bibtype = {article},\n author = {Heikkilä, Janne and Silvén, Olli},\n doi = {10.1142/S0218001496000128},\n journal = {International Journal of Pattern Recognition and Artificial Intelligence},\n number = {2}\n}

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\n \n 1995\n \n \n (1)\n \n \n

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\n \n\n \n \n \n \n \n System considerations for feature tracker based 3-D measurements.\n \n \n \n\n\n \n Heikkilä, J.; and Silvén, O.\n\n\n \n\n\n\n In Proc. Scandinavian Conference on Image Analysis, volume 1, pages 255-262, 1995. PROCEEDINGS PUBLISHED BY VARIOUS PUBLISHERS\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@inproceedings{\n title = {System considerations for feature tracker based 3-D measurements},\n type = {inproceedings},\n year = {1995},\n pages = {255-262},\n volume = {1},\n publisher = {PROCEEDINGS PUBLISHED BY VARIOUS PUBLISHERS},\n id = {8e4ed8f1-a310-3c49-b0e7-6d0406c81d5e},\n created = {2019-09-15T16:34:26.470Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n last_modified = {2019-10-18T15:33:56.744Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Heikkilä, J and Silvén, O},\n booktitle = {Proc. Scandinavian Conference on Image Analysis}\n}

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