Learning feature distance measures for image correspondences

Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005

Volumn II, Issue , 2005, Pages 560-567

  Chen, Xi ^a   Cham, Tat Jen ^a,b  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b SINGAPORE MIT ALLIANCE   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

ERROR ANALYSIS; FEATURE EXTRACTION; IMAGE PROCESSING; PERSONNEL TRAINING; STATISTICAL METHODS;

CLASSIFICATION ERRORS; PARAMETRIC FEATURE DISTANCE MEASURE; SUM-OF-SQUARED PIXEL DIFFERENCES (SSD);

LEARNING SYSTEMS;

EID: 24644468696     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2005.205     Document Type: Conference Paper

References (17)

1. Third IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS), Copenhagen, Denmark, 2002.
2. A. Barla, F. Odone, and A. Verri. Hausdorff kernel for 3D object acquisition and detection. In Proc. Euro. Conf. on Computer Vision, pages 20-33, Copenhagen, Denmark, 2002.
3. H. Barrow, J. Tenenbaum, R. Bolles, and H. Wolf. Parametric correspondence and chamfer matching: Two new techniques for image matching. In Proc. Int. Joint Conf. on Artificial Intelligence, pages 659-663, Cambridge, MA, 1977.
4. X. Chen and T.J. Cham. Discriminative distance measures for image matching. In Proc. Int. Conf. on Pattern Recognition, pages 691-695, Cambridge, England, 2004.
5. E.K.P. Chong and S.H. Zak. An Introduction to Optimization. John Wiley, 2nd edition, 2001.
6. D. Comaniciu, V. Ramesh, and P. Meer. Real-time tracking of non-rigid objects using mean shift. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pages 142-149, Hilton Head, SC, 2000.
7. M.P. Dubuisson and A.K. Jain. A modified hausdorff distance for object matching. In Proc. Int. Conf. on Pattern Recognition, pages 566-568, Jerusalem, Israel, 1994.
8. B. Heisele, P. Ho, and T. Poggio. Face recognition with support vector machines: Global versus component-based approach. In Proc. Int. Conf. on Computer Vision, pages 688-694, Vancouver, Canada, 2001.
9. T. Jebara. Images as bags of pixels. In Proc. Int. Conf. on Computer Vision, pages 265-272, Nice, France, 2003.
10. R. Kondor and T. Jebara. A kernel between sets of vectors. In Proc. Int. Conf. on Machine Learning, pages 361-368, Washington, DC, 2003.
11. D.G. Lowe. Distinctive image features from scale-invariant keypoints. Int. Journal of Computer Vision, 60(2):91-110, 2004.
12. Y. Rubner, C. Tomasi, and L.J. Guibas. The earth mover's distance as a metric for image retrieval. Int. Journal of Computer Vision, 40(2):99-121, 2000.
13. B.W. Silverman. Density Estimation for Statistics and Data Analysis. Chapman and Hall, 1986.
14. T. Sim, S. Baker, and M. Bsat. The CMU Pose, Illumination, and Expression database. IEEE Trans. Pattern Analysis and Machine Intelligence, 25(12):1615-1618, 2003.
15. K. Toyama and A. Blake. Probabilistic tracking in a metric space. In Proc. Int. Conf. on Computer Vision, pages 50-57, Vancouver, Canada, 2001.
16. P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pages 511-518, Kauai, HI, 2001.
17. L. Wolf and A. Shashua. Kernel principal angles for classification machines with applications to image sequence interpretation. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pages 635-640, Madison, WI, 2003.