Generalized Projection Based M-Estimator.

Sushil Mittal, Saket Anand and Peter Meer
Department of Electrical and Computer Engineering
Rutgers University, Piscataway, NJ 08854, USA

We propose a novel robust estimation algorithm - the generalized projection based M-estimator (gpbM), which does not require the user to specify any scale parameters. The algorithm is general and can handle heteroscedastic data with multiple linear constraints for single and multi-carrier problems. The gpbM has three distinct stages - scale estimation, robust model estimation and inlier/outlier dichotomy. In contrast, in its predecessor pbM, each model hypotheses was associated with a different scale estimate. For data containing multiple inlier structures with generally different noise covariances, the estimator iteratively determines one structure at a time. The model estimation can be further optimized by using Grassmann manifold theory. We present several homoscedastic and heteroscedastic synthetic and real-world computer vision problems with single and multiple carriers.

IEEE Trans. Pattern Anal. Machine Intell., 34, 2351-2364, 2012.
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