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.
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