Abstract We propose a solution to the problem of robust subspace estimation using the projection based M-estimator. The new method handles more outliers than inliers, does not require a user defined scale of the noise affecting the inliers, handles noncentered data and nonorthogonal subspaces. Other robust methods like RANSAC, use an input for the scale, while methods for subspace segmentation, like GPCA, are not robust. Synthetic data and three real cases of multibody factorization show the superiority of our method, in spite of user independence.
Subspace Estimation Using Projection Based M-Estimators over Grassmann Manifolds
Computer Vision – ECCV 2006 ; 3 ; 301-312
Lecture Notes in Computer Science ; 3951 , 3
2006-01-01
12 pages
Article/Chapter (Book)
Electronic Resource
English
Subspace Estimation Using Projection Based M-Estimators over Grassmann Manifolds
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