Generalized projection based M-Estimator: Theory and applications.
Sushil Mittal, Saket Anand and Peter Meer
Department of Electrical and Computer Engineering
Rutgers University, Piscataway, NJ 08854, USA
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.
2011 Computer Vision and Pattern Recognition Conference,
Colorado Springs, CO, June 2011, 2689-2696.
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