Robust Regression with Projection Based M-estimators

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.

• Type: Conference
• Year: 2003
• Where: ICCV
• Authors: Haifeng Chen, Peter Meer
• Keywords: Computer Vision, ICCV 2003, Matrix Estimation Tasks, Optimal Threshold, Projection Pursuit Optimization, Robust Regression Techniques, Univariate Kernel Density