A robust ellipse fitting algorithm based on sparsity of outliers. Sobhani, E., Sadeghi, M., Babaie-Zadeh, M., & Jutten, C. In 2017 25th European Signal Processing Conference (EUSIPCO), pages 1195-1199, Aug, 2017.
Paper doi abstract bibtex 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.
@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},
}
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