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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