Robust Regression for Data with Multiple Structures

Haifeng Chen Peter Meer

Department of Electrical and Computer Engineering
Rutgers University, Piscataway, NJ 08854, USA

Two new techniques based on nonparametric estimation of probability densities are introduced which improve on the performance of equivalent robust methods currently employed in computer vision. The first technique draws from the projection pursuit paradigm in statistics, and carries out regression M-estimation with a weak dependence on the accuracy of the scale estimate. The second technique exploits the properties of the multivariate adaptive mean shift, and accomplishes the fusion of uncertain measurements arising from an unknown number of sources. As an example, the two techniques are extensively used in an algorithm for the recovery of multiple structures from heavily corrupted data.

7th European Conference on Computer Vision, Copenhagen, May 2002, vol. I, 236-250.
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