Title :
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
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;
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
DOI :
10.1109/CAMSAP.2009.5413262
