Robust Regression with Projection Based M-estimators
Haifeng Chen and Peter Meer
Department of Electrical and Computer Engineering
Rutgers University, Piscataway, NJ 08854, USA
The robust regression techniques in the RANSAC family are
popular today in computer vision, but their performance depends on a
user supplied threshold. We eliminate this drawback of RANSAC by
reformulating another robust method, the M-estimator,
as a projection pursuit optimization problem.
The projection based pbM-estimator automatically derives the
threshold from univariate kernel density estimates.
Nevertheless, the performance of the pbM-estimator
equals or exceeds that of RANSAC techniques tuned to the optimal
threshold, a value which is never available in practice.
Experiments were performed both with synthetic and
real data in the affine motion and fundamental matrix
estimation tasks.
9th International Conference on Computer Vision
, Nice, France, October 2003, 878-885.
Return to Research: Robust Analysis of Visual Data
