Department of Electrical and Computer Engineering
Rutgers University, Piscataway, NJ 08854, USA
Robust regression methods, such as RANSAC, suffer from
a sensitivity to the scale parameter used for generating
the inlier-outlier dichotomy. Projection based M-estimators
(pbM) offer a solution to this by reframing the regression
problem in a projection pursuit framework. In this paper
we modify the pbM formulation to obtain an improved pbM
algorithm. Furthermore, the modified algorithm is easily
generalized to handle heteroscedastic data . The superior
performance of heteroscedastic pbM, as compared to simple
pbM, is experimentally verified.
Workshop on Empirical evaluation Methods in Computer Vision, San Diego, CA, June 2005 (in conjunction with CVPR'05 ).
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