Ellipse Fitting of Planar Points With Outliers Using Random Samples Filtered by Fitting Qualities

ABSTRACT Ellipse fitting is a traditional approach to construct elliptical models from points. Outliers can significantly distort a fittied ellipse from the actual model. A novel ellipse fitting algorithm is proposed to be applied to planar points with outliers. The fitting qualities of candidate ellipses generated by five‐point random sampling are evaluated, and the median curve of the best fittings yields the final result through a classic fitting algorithm. A fitting error metric is introduced. The proposed algorithm achieved median fitting errors of 0.067 on a synthetic dataset and 0.057 on an image dataset, respectively, both the best among the algorithms compared. The execution speed of the novel algorithm is on average 0.091 s on the synthetic dataset and 0.087 s on the image dataset. The algorithm is advantageous also for use due to the comprehensibility and insensitivity of the algorithmic parameters.