2009/10/24 by Jieqi Yu, Yu, Jieqi, Haipeng Zheng +5
Mathematics · #FOS: Computer and information sciences #Methodology (stat.ME) #stat.ME
paper · pdf · doi:10.48550/arxiv.0910.4610
4 pages, 9 figures, accepted by The Third International Workshop on Computational Advances in Multi-Sensor Adaptive Processing
arxiv created 2009/10/24 · arxiv updated 2009/12/01
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.