Robust fitting of ellipses and spheroids

Authors:
Jieqi Yu;Sanjeev R. Kulkarni;H. Vincent Poor
Affiliations:
Department of Electrical Engineering, Princeton University, Princeton, NJ;Department of Electrical Engineering, Princeton University, Princeton, NJ;Department of Electrical Engineering, Princeton University, Princeton, NJ
Venue:
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Year:
2009

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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.