A robust ellipse fitting algorithm based on sparsity of outliers

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.