Robust regression with projection based M-estimators

Haifeng Chen, Peter Meer

Research output: Contribution to conferencePaperpeer-review

73 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages878-885
Number of pages8
StatePublished - 2003
EventNINTH IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION - Nice, France
Duration: Oct 13 2003Oct 16 2003

Other

OtherNINTH IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION
Country/TerritoryFrance
CityNice
Period10/13/0310/16/03

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

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