@InProceedings{8081397,
  author = {E. Sobhani and M. Sadeghi and M. Babaie-Zadeh and C. Jutten},
  booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},
  title = {A robust ellipse fitting algorithm based on sparsity of outliers},
  year = {2017},
  pages = {1195-1199},
  abstract = {Ellipse fitting is widely used in computer vision and pattern recognition algorithms such as object segmentation and pupil/eye tracking. Generally, ellipse fitting is finding the best ellipse parameters that can be fitted on a set of data points, which are usually noisy and contain outliers. The algorithms of fitting the best ellipse should be both suitable for real-time applications and robust against noise and outliers. In this paper, we introduce a new method of ellipse fitting which is based on sparsity of outliers and robust Huber's data fitting measure. We will see that firstly this approach is theoretically better justified than a state-of-the-art ellipse fitting algorithm based on sparse representation. Secondly, simulation results show that it provides a better robustness against outliers compared to some previous ellipse fitting approaches, while being even faster.},
  keywords = {computer vision;curve fitting;image representation;image segmentation;pattern recognition;sparse representation;robust Huber data fitting measure;real-time applications;pupil-eye tracking;outliers sparsity;previous ellipse fitting approaches;state-of-the-art ellipse;ellipse parameters;object segmentation;pattern recognition algorithms;computer vision;robust ellipse fitting algorithm;Robustness;Minimization;Gaussian noise;Signal processing algorithms;Mathematical model;Dictionaries;Europe},
  doi = {10.23919/EUSIPCO.2017.8081397},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570346840.pdf},
}

{"_id":"t2Zn6viKtxzWQAdzM","bibbaseid":"sobhani-sadeghi-babaiezadeh-jutten-arobustellipsefittingalgorithmbasedonsparsityofoutliers-2017","authorIDs":[],"author_short":["Sobhani, E.","Sadeghi, M.","Babaie-Zadeh, M.","Jutten, C."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["E."],"propositions":[],"lastnames":["Sobhani"],"suffixes":[]},{"firstnames":["M."],"propositions":[],"lastnames":["Sadeghi"],"suffixes":[]},{"firstnames":["M."],"propositions":[],"lastnames":["Babaie-Zadeh"],"suffixes":[]},{"firstnames":["C."],"propositions":[],"lastnames":["Jutten"],"suffixes":[]}],"booktitle":"2017 25th European Signal Processing Conference (EUSIPCO)","title":"A robust ellipse fitting algorithm based on sparsity of outliers","year":"2017","pages":"1195-1199","abstract":"Ellipse fitting is widely used in computer vision and pattern recognition algorithms such as object segmentation and pupil/eye tracking. Generally, ellipse fitting is finding the best ellipse parameters that can be fitted on a set of data points, which are usually noisy and contain outliers. The algorithms of fitting the best ellipse should be both suitable for real-time applications and robust against noise and outliers. In this paper, we introduce a new method of ellipse fitting which is based on sparsity of outliers and robust Huber's data fitting measure. We will see that firstly this approach is theoretically better justified than a state-of-the-art ellipse fitting algorithm based on sparse representation. Secondly, simulation results show that it provides a better robustness against outliers compared to some previous ellipse fitting approaches, while being even faster.","keywords":"computer vision;curve fitting;image representation;image segmentation;pattern recognition;sparse representation;robust Huber data fitting measure;real-time applications;pupil-eye tracking;outliers sparsity;previous ellipse fitting approaches;state-of-the-art ellipse;ellipse parameters;object segmentation;pattern recognition algorithms;computer vision;robust ellipse fitting algorithm;Robustness;Minimization;Gaussian noise;Signal processing algorithms;Mathematical model;Dictionaries;Europe","doi":"10.23919/EUSIPCO.2017.8081397","issn":"2076-1465","month":"Aug","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570346840.pdf","bibtex":"@InProceedings{8081397,\n author = {E. Sobhani and M. Sadeghi and M. Babaie-Zadeh and C. Jutten},\n booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},\n title = {A robust ellipse fitting algorithm based on sparsity of outliers},\n year = {2017},\n pages = {1195-1199},\n abstract = {Ellipse fitting is widely used in computer vision and pattern recognition algorithms such as object segmentation and pupil/eye tracking. Generally, ellipse fitting is finding the best ellipse parameters that can be fitted on a set of data points, which are usually noisy and contain outliers. The algorithms of fitting the best ellipse should be both suitable for real-time applications and robust against noise and outliers. In this paper, we introduce a new method of ellipse fitting which is based on sparsity of outliers and robust Huber's data fitting measure. We will see that firstly this approach is theoretically better justified than a state-of-the-art ellipse fitting algorithm based on sparse representation. Secondly, simulation results show that it provides a better robustness against outliers compared to some previous ellipse fitting approaches, while being even faster.},\n keywords = {computer vision;curve fitting;image representation;image segmentation;pattern recognition;sparse representation;robust Huber data fitting measure;real-time applications;pupil-eye tracking;outliers sparsity;previous ellipse fitting approaches;state-of-the-art ellipse;ellipse parameters;object segmentation;pattern recognition algorithms;computer vision;robust ellipse fitting algorithm;Robustness;Minimization;Gaussian noise;Signal processing algorithms;Mathematical model;Dictionaries;Europe},\n doi = {10.23919/EUSIPCO.2017.8081397},\n issn = {2076-1465},\n month = {Aug},\n url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570346840.pdf},\n}\n\n","author_short":["Sobhani, E.","Sadeghi, M.","Babaie-Zadeh, M.","Jutten, C."],"key":"8081397","id":"8081397","bibbaseid":"sobhani-sadeghi-babaiezadeh-jutten-arobustellipsefittingalgorithmbasedonsparsityofoutliers-2017","role":"author","urls":{"Paper":"https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570346840.pdf"},"keyword":["computer vision;curve fitting;image representation;image segmentation;pattern recognition;sparse representation;robust Huber data fitting measure;real-time applications;pupil-eye tracking;outliers sparsity;previous ellipse fitting approaches;state-of-the-art ellipse;ellipse parameters;object segmentation;pattern recognition algorithms;computer vision;robust ellipse fitting algorithm;Robustness;Minimization;Gaussian noise;Signal processing algorithms;Mathematical model;Dictionaries;Europe"],"metadata":{"authorlinks":{}},"downloads":0},"bibtype":"inproceedings","biburl":"https://raw.githubusercontent.com/Roznn/EUSIPCO/main/eusipco2017url.bib","creationDate":"2021-02-13T16:38:25.636Z","downloads":0,"keywords":["computer vision;curve fitting;image representation;image segmentation;pattern recognition;sparse representation;robust huber data fitting measure;real-time applications;pupil-eye tracking;outliers sparsity;previous ellipse fitting approaches;state-of-the-art ellipse;ellipse parameters;object segmentation;pattern recognition algorithms;computer vision;robust ellipse fitting algorithm;robustness;minimization;gaussian noise;signal processing algorithms;mathematical model;dictionaries;europe"],"search_terms":["robust","ellipse","fitting","algorithm","based","sparsity","outliers","sobhani","sadeghi","babaie-zadeh","jutten"],"title":"A robust ellipse fitting algorithm based on sparsity of outliers","year":2017,"dataSources":["2MNbFYjMYTD6z7ExY","uP2aT6Qs8sfZJ6s8b"]}