Robust Fitting of Ellipses and Spheroids
Abstract
Ellipse and ellipsoid fitting has been extensively researched and widely applied. Although traditional fitting methods provide accurate estimation of ellipse parameters in the low-noise case, their performance is compromised when the noise level or the ellipse eccentricity are high. A series of robust fitting algorithms are proposed that perform well in high-noise, high-eccentricity ellipse/spheroid (a special class of ellipsoid) cases. The new algorithms are based on the geometric definition of an ellipse/spheroid, and improved using global statistical properties of the data. The efficacy of the new algorithms is demonstrated through simulations.
Cite
@article{arxiv.0912.1647,
title = {Robust Fitting of Ellipses and Spheroids},
author = {Jieqi Yu and Sanjeev R. Kulkarni and H. Vincent Poor},
journal= {arXiv preprint arXiv:0912.1647},
year = {2009}
}
Comments
in proceeding of 43rd Asilomar Conference on Signals, Systems and Computers, Pacific Grove, California, 2009