A robust ellipse fitting algorithm based on sparsity of outliers.
European Signal Processing Conference(2017)
摘要
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.
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关键词
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
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