Department of Electrical and Computer Engineering
Rutgers University, Piscataway, NJ 08854, USA
RANSAC is the most widely used robust regression algorithm
in computer vision. However, RANSAC has a few
drawbacks which make it difficult to use in a lot of applications.
Some of these problems have been addressed through
improved sampling algorithms or better cost functions, but
an important problem still remains. The algorithms are not
user independent, and require some knowledge of the scale
of the inlier noise. The projection based M-estimator (pbM)
offers a solution to this by reframing the regression problem
in a projection pursuit framework. In this paper we derive
the pbM algorithm for heteroscedastic data. Our algorithm
is applied to various real problems and its performance is
compared with RANSAC and MSAC. It is shown that pbM
gives better results than RANSAC and MSAC in spite of being
user independent.
Workshop on 25 Years of RANSAC, New York, NY, June 2006 (in conjunction with CVPR'06 ).
Return to Research: Robust Analysis of Visual Data
