Robust regression with projection based M-estimators

Proceedings of the IEEE International Conference on Computer Vision

Volumn 2, Issue , 2003, Pages 878-885

  Chen, Haifeng ^a   Meer, Peter ^a  

^a RUTGERS UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MATHEMATICAL MODELS; MOTION ESTIMATION; OPTIMIZATION; PROBABILITY DENSITY FUNCTION; REGRESSION ANALYSIS;

PROJECTION BASED M-ESTIMATOR; ROBUST REGRESSION;

COMPUTER VISION;

EID: 0344552324     PISSN: 15505499     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. H. Chen and P. Meer. Robust computer vision through kernel density estimation. In 7th European Conference on Computer Vision, volume I, pages 236-250, Copenhagen, Denmark, May 2002.
2. H. Chen, P. Meer, and D. E. Tyler. Robust regression for data with multiple structures. In 2001 IEEE Conference on Computer Vision and Pattern Recognition, volume I, pages 1069-1075, Kauai, HI, December 2001.
3. R. O. Duda, P. E. Hart, and D. G. Stork. Pattern Classification. Wiley, second edition, 2000.
4. M. A. Fischler and R. C. Bolles. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Comm. Assoc. Comp. Mach, 24(6):381-395, 1981.
5. P. J. Huber. Projection pursuit (with discussion). Annals of Statistics, 13:435-525, 1985.
6. M. C. Jones and R. Sibson. What is projection pursuit? (with discussion). J. Royal Stat. Soc. A, 150:1-37, 1987.
7. E.-Y. Kang, I. Cohen, and G. Medioni. Robust affine motion estimation in joint image space using tensor voting. In 16th Intern. Conf. on Computer Vision and Pattern Recogn., volume IV, pages 256-259, Quebec City, Canada, August 2002.
8. Y. Leedan and P. Meer. Heteroscedastic regression in computer vision: Problems with bilinear constraint. International J. of Computer Vision, 37:127-150, 2000.
9. R. M. Lewis, V. Torczon, and M. W. Trosset. Direct search methods: Then and now. J. Computational and Applied Math., 124:191-207, 2000.
10. S. Z. Li. Robustizing robust M-estimation using deterministic annealing. Pattern Recog., 29:159-166, 1996.
11. W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery. Numerical Recipes in C. Cambridge University Press, second edition, 1992.
12. P. J. Rousseeuw and A. M. Leroy. Robust Regression and Outlier Detection. Wiley, 1987.
13. D. Ruppert and D. G. Simpson. Comment on "Unmasking Multivariate Outliers and Leverage Points", by P. J. Rousseeuw and B. C. van Zomeren. J. of Amer. Stat. Assoc., 85:644-646, 1990.
14. B. Tordoff and D.W. Murray. Guided sampling and consensus for motion estimation. In 7th European Conference on Computer Vision, volume I, pages 82-96, Copenhagen, Denmark, May 2002.
15. P. H. S. Torr and D. W. Murray. The development and comparison of robust methods for estimating the fundamental matrix. International J. of Computer Vision, 24(3):271-300, 1997.
16. P. H. S. Torr and A. Zisserman. MLESAC: A new robust estimator with application to estimating image geometry. Computer Vision and Image Understanding, 78:138-156, 2000.
17. M. P. Wand and M. C. Jones. Kernel Smoothing. Chapman & Hall, 1995.
18. Z. Zhang. Software package for epipolar geometry computation. Available at www-sop.inria.fr/robotvis/personnel/zzhang/software-FMatrix.html.
19. Z. Zhang. Determining the epipolar geometry and its uncertainty: A review. International J. of Computer Vision, 27:161-195, 1998.