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\n\n \n \n \n \n \n \n Evaluation of 2D-/3D-Feet-Detection Methods for Semi-Autonomous Powered Wheelchair Navigation.\n \n \n \n \n\n\n \n Giménez, C. V.; Krug, S.; Qureshi, F. Z.; and O’Nils, M.\n\n\n \n\n\n\n
Journal of Imaging, 7(12). 2021.\n
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@Article{21-jimaging-j2,\nAUTHOR = {Giménez, Cristian Vilar and Krug, Silvia and Qureshi, Faisal Z. and O’Nils, Mattias},\nTITLE = {Evaluation of 2D-/3D-Feet-Detection Methods for Semi-Autonomous Powered Wheelchair Navigation},\nJOURNAL = {Journal of Imaging},\nVOLUME = {7},\nYEAR = {2021},\nNUMBER = {12},\nARTICLE-NUMBER = {255},\nURL = {https://www.mdpi.com/2313-433X/7/12/255},\nISSN = {2313-433X},\nABSTRACT = {Powered wheelchairs have enhanced the mobility and quality of life of people with special needs. The next step in the development of powered wheelchairs is to incorporate sensors and electronic systems for new control applications and capabilities to improve their usability and the safety of their operation, such as obstacle avoidance or autonomous driving. However, autonomous powered wheelchairs require safe navigation in different environments and scenarios, making their development complex. In our research, we propose, instead, to develop contactless control for powered wheelchairs where the position of the caregiver is used as a control reference. Hence, we used a depth camera to recognize the caregiver and measure at the same time their relative distance from the powered wheelchair. In this paper, we compared two different approaches for real-time object recognition using a 3DHOG hand-crafted object descriptor based on a 3D extension of the histogram of oriented gradients (HOG) and a convolutional neural network based on YOLOv4-Tiny. To evaluate both approaches, we constructed Miun-Feet—a custom dataset of images of labeled caregiver’s feet in different scenarios, with backgrounds, objects, and lighting conditions. The experimental results showed that the YOLOv4-Tiny approach outperformed 3DHOG in all the analyzed cases. In addition, the results showed that the recognition accuracy was not improved using the depth channel, enabling the use of a monocular RGB camera only instead of a depth camera and reducing the computational cost and heat dissipation limitations. Hence, the paper proposes an additional method to compute the caregiver’s distance and angle from the Powered Wheelchair (PW) using only the RGB data. This work shows that it is feasible to use the location of the caregiver’s feet as a control signal for the control of a powered wheelchair and that it is possible to use a monocular RGB camera to compute their relative positions.},\nDOI = {10.3390/jimaging7120255},\nurl_Paper = {pubs/21-jimaging-j2.pdf},\nkeywords = {feet-localization}\n}\n\n
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\n Powered wheelchairs have enhanced the mobility and quality of life of people with special needs. The next step in the development of powered wheelchairs is to incorporate sensors and electronic systems for new control applications and capabilities to improve their usability and the safety of their operation, such as obstacle avoidance or autonomous driving. However, autonomous powered wheelchairs require safe navigation in different environments and scenarios, making their development complex. In our research, we propose, instead, to develop contactless control for powered wheelchairs where the position of the caregiver is used as a control reference. Hence, we used a depth camera to recognize the caregiver and measure at the same time their relative distance from the powered wheelchair. In this paper, we compared two different approaches for real-time object recognition using a 3DHOG hand-crafted object descriptor based on a 3D extension of the histogram of oriented gradients (HOG) and a convolutional neural network based on YOLOv4-Tiny. To evaluate both approaches, we constructed Miun-Feet—a custom dataset of images of labeled caregiver’s feet in different scenarios, with backgrounds, objects, and lighting conditions. The experimental results showed that the YOLOv4-Tiny approach outperformed 3DHOG in all the analyzed cases. In addition, the results showed that the recognition accuracy was not improved using the depth channel, enabling the use of a monocular RGB camera only instead of a depth camera and reducing the computational cost and heat dissipation limitations. Hence, the paper proposes an additional method to compute the caregiver’s distance and angle from the Powered Wheelchair (PW) using only the RGB data. This work shows that it is feasible to use the location of the caregiver’s feet as a control signal for the control of a powered wheelchair and that it is possible to use a monocular RGB camera to compute their relative positions.\n
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\n\n \n \n \n \n \n \n A Temporal Boosted YOLO-Based Model for Birds Detection around Wind Farms.\n \n \n \n \n\n\n \n Alqaysi, H.; Fedorov, I.; Qureshi, F. Z.; and O’Nils, M.\n\n\n \n\n\n\n
Journal of Imaging, 7(11): 13pp. 2021.\n
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@Article{21-jimaging-j,\nAUTHOR = {Alqaysi, Hiba and Fedorov, Igor and Qureshi, Faisal Z. and O’Nils, Mattias},\nTITLE = {A Temporal Boosted YOLO-Based Model for Birds Detection around Wind Farms},\nJOURNAL = {Journal of Imaging},\nVOLUME = {7},\nYEAR = {2021},\nNUMBER = {11},\npages = {13pp},\nARTICLE-NUMBER = {227},\nURL = {https://www.mdpi.com/2313-433X/7/11/227},\nISSN = {2313-433X},\nABSTRACT = {Object detection for sky surveillance is a challenging problem due to having small objects in a large volume and a constantly changing background which requires high resolution frames. For example, detecting flying birds in wind farms to prevent their collision with the wind turbines. This paper proposes a YOLOv4-based ensemble model for bird detection in grayscale videos captured around wind turbines in wind farms. In order to tackle this problem, we introduce two datasets—(1) Klim and (2) Skagen—collected at two locations in Denmark. We use Klim training set to train three increasingly capable YOLOv4 based models. Model 1 uses YOLOv4 trained on the Klim dataset, Model 2 introduces tiling to improve small bird detection, and the last model uses tiling and temporal stacking and achieves the best mAP values on both Klim and Skagen datasets. We used this model to set up an ensemble detector, which further improves mAP values on both datasets. The three models achieve testing mAP values of 82%, 88%, and 90% on the Klim dataset. mAP values for Model 1 and Model 3 on the Skagen dataset are 60% and 92%. Improving object detection accuracy could mitigate birds’ mortality rate by choosing the locations for such establishment and the turbines location. It can also be used to improve the collision avoidance systems used in wind energy facilities.},\nDOI = {10.3390/jimaging7110227},\nurl_Paper = {pubs/21-jimaging-j.pdf},\nkeywords = {bird-detection}\n}\n\n
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\n Object detection for sky surveillance is a challenging problem due to having small objects in a large volume and a constantly changing background which requires high resolution frames. For example, detecting flying birds in wind farms to prevent their collision with the wind turbines. This paper proposes a YOLOv4-based ensemble model for bird detection in grayscale videos captured around wind turbines in wind farms. In order to tackle this problem, we introduce two datasets—(1) Klim and (2) Skagen—collected at two locations in Denmark. We use Klim training set to train three increasingly capable YOLOv4 based models. Model 1 uses YOLOv4 trained on the Klim dataset, Model 2 introduces tiling to improve small bird detection, and the last model uses tiling and temporal stacking and achieves the best mAP values on both Klim and Skagen datasets. We used this model to set up an ensemble detector, which further improves mAP values on both datasets. The three models achieve testing mAP values of 82%, 88%, and 90% on the Klim dataset. mAP values for Model 1 and Model 3 on the Skagen dataset are 60% and 92%. Improving object detection accuracy could mitigate birds’ mortality rate by choosing the locations for such establishment and the turbines location. It can also be used to improve the collision avoidance systems used in wind energy facilities.\n
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\n\n \n \n \n \n \n \n Improving hyperspectral image segmentation by applying inverse noise weighting and outlier removal for optimal scale selection.\n \n \n \n \n\n\n \n Dao, P. D.; Mantripragada, K.; He, Y.; and Qureshi, F. Z.\n\n\n \n\n\n\n
ISPRS Journal of Photogrammetry and Remote Sensing, 171: 348 - 366. 2021.\n
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@article{20-photo-j,\ntitle = {Improving hyperspectral image segmentation by applying inverse noise weighting and outlier removal for optimal scale selection},\njournal = {ISPRS Journal of Photogrammetry and Remote Sensing},\nvolume = {171},\npages = {348 - 366},\nyear = {2021},\nissn = {0924-2716},\ndoi = {https://doi.org/10.1016/j.isprsjprs.2020.11.013},\nurl = {http://www.sciencedirect.com/science/article/pii/S0924271620303208},\nauthor = {Phuong D. Dao and Kiran Mantripragada and Yuhong He and Faisal Z. Qureshi},\nkeywords = {hyperspectral-segmentation},\nabstract = {Optimal scale selection for image segmentation is an essential component of the Object-Based Image Analysis (OBIA) and interpretation. An optimal segmentation scale is a scale at which image objects, overall, best represent real-world ground objects and features across the entire image. At this scale, the intra-object variance is ideally lowest and the inter-object spatial autocorrelation is ideally highest, and a change in the scale could cause an abrupt change in these measures. Unsupervised parameter optimization methods typically use global measures of spatial and spectral properties calculated from all image objects in all bands as the target criteria to determine the optimal segmentation scale. However, no studies consider the effect of noise in image spectral bands on the segmentation assessment and scale selection. Furthermore, these global measures could be affected by outliers or extreme values from a small number of objects. These issues may lead to incorrect assessment and selection of optimal scales and cause the uncertainties in subsequent segmentation and classification results. These issues become more pronounced when segmenting hyperspectral data with large spectral variability across the spectrum. In this study, we propose an enhanced method that 1) incorporates the band’s inverse noise weighting in the segmentation and 2) detects and removes outliers before determining segmentation scale parameters. The proposed method is evaluated on three well-established segmentation approaches – k-means, mean-shift, and watershed. The generated segments are validated by comparing them with reference polygons using normalized over-segmentation (OS), under-segmentation (US), and the Euclidean Distance (ED) indices. The results demonstrate that this proposed scale selection method produces more accurate and reliable segmentation results. The approach can be applied to other segmentation selection criteria and are useful for automatic multi-parameter tuning and optimal scale parameter selections in OBIA methods in remote sensing.}\n}\n\n
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\n Optimal scale selection for image segmentation is an essential component of the Object-Based Image Analysis (OBIA) and interpretation. An optimal segmentation scale is a scale at which image objects, overall, best represent real-world ground objects and features across the entire image. At this scale, the intra-object variance is ideally lowest and the inter-object spatial autocorrelation is ideally highest, and a change in the scale could cause an abrupt change in these measures. Unsupervised parameter optimization methods typically use global measures of spatial and spectral properties calculated from all image objects in all bands as the target criteria to determine the optimal segmentation scale. However, no studies consider the effect of noise in image spectral bands on the segmentation assessment and scale selection. Furthermore, these global measures could be affected by outliers or extreme values from a small number of objects. These issues may lead to incorrect assessment and selection of optimal scales and cause the uncertainties in subsequent segmentation and classification results. These issues become more pronounced when segmenting hyperspectral data with large spectral variability across the spectrum. In this study, we propose an enhanced method that 1) incorporates the band’s inverse noise weighting in the segmentation and 2) detects and removes outliers before determining segmentation scale parameters. The proposed method is evaluated on three well-established segmentation approaches – k-means, mean-shift, and watershed. The generated segments are validated by comparing them with reference polygons using normalized over-segmentation (OS), under-segmentation (US), and the Euclidean Distance (ED) indices. The results demonstrate that this proposed scale selection method produces more accurate and reliable segmentation results. The approach can be applied to other segmentation selection criteria and are useful for automatic multi-parameter tuning and optimal scale parameter selections in OBIA methods in remote sensing.\n
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