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Generalized Projection Based M-Estimator: Theory and Applications

8 years 3 months ago
Generalized Projection Based M-Estimator: Theory and Applications
We introduce a robust estimator called generalized projection based M-estimator (gpbM) which does not require the user to specify any scale parameters. For multiple inlier structures, with different noise covariances, the estimator iteratively determines one inlier structure at a time. Unlike pbM, where the scale of the inlier noise is estimated simultaneously with the model parameters, gpbM has three distinct stages – scale estimation, robust model estimation and inlier/outlier dichotomy. We evaluate our performance on challenging synthetic data, face image clustering upto ten different faces from Yale Face Database B and multi-body projective motion segmentation problem on Hopkins155 dataset. Results of state-of-the-art methods are presented for comparison.
Sushil Mittal, Saket Anand, Peter Meer
Added 28 Mar 2011
Updated 29 Apr 2011
Type Journal
Year 2011
Where CVPR
Authors Sushil Mittal, Saket Anand, Peter Meer
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