Generalized projection based M-estimator: Theory and applications

Sushil Mittal, Saket Anand, Peter Meer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations

Abstract

We introduce a robust estimator called generalized projection based M-estimator (gpbM) which does not require the user to specify any scale parameters. For multiple inlier structures, with different noise covariances, the estimator iteratively determines one inlier structure at a time. Unlike pbM, where the scale of the inlier noise is estimated simultaneously with the model parameters, gpbM has three distinct stages-scale estimation, robust model estimation and inlier/outlier dichotomy. We evaluate our performance on challenging synthetic data, face image clustering upto ten different faces from Yale Face Database B and multi-body projective motion segmentation problem on Hopkins155 dataset. Results of state-of-the-art methods are presented for comparison.

Original languageEnglish (US)
Title of host publication2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011
PublisherIEEE Computer Society
Pages2689-2696
Number of pages8
ISBN (Print)9781457703942
DOIs
StatePublished - Jan 1 2011

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

Cite this

Mittal, S., Anand, S., & Meer, P. (2011). Generalized projection based M-estimator: Theory and applications. In 2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011 (pp. 2689-2696). [5995514] (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition). IEEE Computer Society. https://doi.org/10.1109/CVPR.2011.5995514