Outlier elimination for robust ellipse and ellipsoid fitting

Author

Yu, Jieqi ; Zheng, Haipeng ; Kulkarni, Sanjeev R. ; Poor, H. Vincent

Author_Institution

Dept. of Electr. Eng., Princeton Univ., Princeton, NJ, USA

fYear

2009

fDate

13-16 Dec. 2009

Firstpage

33

Lastpage

36

Abstract

In this paper, an outlier elimination algorithm for ellipse/ellipsoid fitting is proposed. This two-stage algorithm employs a proximity-based outlier detection algorithm (using the graph Laplacian), followed by a model-based outlier detection algorithm similar to random sample consensus (RANSAC). These two stages compensate for each other so that outliers of various types can be eliminated with reasonable computation. The outlier elimination algorithm considerably improves the robustness of ellipse/ellipsoid fitting as demonstrated by simulations.

Keywords

curve fitting; elliptic equations; Laplacian graph; RANSAC; ellipsoid fitting; outlier elimination algorithm; proximity based outlier detection algorithm; random sample consensus; robust ellipse fitting; Computational modeling; Conferences; Counting circuits; Detection algorithms; Ellipsoids; Laplace equations; Machine learning; Neural networks; Robustness; Statistics;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2009 3rd IEEE International Workshop on

Conference_Location

Aruba, Dutch Antilles

Print_ISBN

978-1-4244-5179-1

Electronic_ISBN

978-1-4244-5180-7

Type

conf

DOI

10.1109/CAMSAP.2009.5413262

Filename

5413262