Outlier Elimination for Robust Ellipse and Ellipsoid Fitting

Autor:	Yu, Jieqi, Zheng, Haipeng, Kulkarni, Sanjeev R., Poor, H. Vincent
Rok vydání:	2009
Předmět:	Statistics - Methodology
Druh dokumentu:	Working Paper
Popis:	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. Comment: 4 pages, 9 figures, accepted by The Third International Workshop on Computational Advances in Multi-Sensor Adaptive Processing
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/0910.4610 Zobrazit plný text záznamu View this record from Arxiv