The layout consistent random field for recognizing and segmenting partially occluded objects

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn 1, Issue , 2006, Pages 37-44

  Winn, John ^a   Shotton, Jamie ^b  

^a MICROSOFT RESEARCH   (United Kingdom)

^b UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

LAYOUT CONSISTENT RANDOM FIELD; LAYOUT CONSISTENT RANDOM FIELD (LAYOUTCRF); OBJECT DEFORMATION; PARTIAL OCCLUSION;

ALGORITHMS; CONSTRAINT THEORY; IMAGE SEGMENTATION; PROBLEM SOLVING;

OBJECT RECOGNITION;

EID: 33845597355     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2006.305     Document Type: Conference Paper

References (18)

1. S. Agarwal and D. Roth. Learning a sparse representation for object detection. In European Conference on Computer Vision, 2002.
2. E. Borenstein, E. Sharon, and S. Ullman. Combining top-down and bottom-up segmentation. In Proceedings IEEE workshop on Perceptual Organization in Computer Vision, CVPR 2004, 2004.
3. Y. Boykov and M.-P. Jolly. Interactive graph cuts for optimal boundary and region segmentation of objects in n-d images. Proc. of IEEE ICCV, 2001.
4. D. Crandall, P. Felzenszwalb, and D. Huttenlocher. Spatial priors for part-based recognition using statistical models. In CVPR, 2003.
5. R. Fergus, P. Perona, and A. Zisserman. Object class recognition by unsupervised scale-invariant learning. In Computer Vision and Pattern Recognition, 2003.
6. A. Kapoor and J. Winn. Located hidden random fields learning discriminative parts for object detection. In European Conference on Computer Vision, 2006.
7. V. Kolmogorov and R. Zabih. What energy functions can be minimized via graph cuts? In IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 26, February 2004.
8. M. P. Kumar, P. H. S. Torr, and A. Zisserman. OBJ CUT. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, 2005.
9. S. Kumar and M. Hebert. Discriminative random fields: A discriminative framework for contextual interaction in classification. In ICCV, 2003.
10. J. Lafferty, A. McCallum, and F. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In International Conference on Machine Learning, 2001.
11. B. Leibe, A. Leonardis, and B. Schiele. Combined object categorization and segmentation with an implicit shape model. In ECCV'04 Workshop om Statistical Learning in Computer Vision, May 2004.
12. V. Lepetit, P. Lagger, and P. Fua. Randomized trees for real-time keypoint recognition. In CVPR05, pages II: 775-781, 2005.
13. A. Martinez and R. Benavente. The AR face database. Technical Report 24, CVC, June 1998.
14. A. Quattoni, M. Collins, and T. Darrell. Conditional random fields for object recognition. In NIPS, 2004.
15. J. Shotton, A. Blake, and R. Cipolla. Contour-based learning for object detection. In ICCV, 2005.
16. M. Szummer. Learning diagram parts with hidden random fields. In International Conference on Document Analysis and Recognition, 2005.
17. O. M. C. Williams, A. Blake, and R. Cipolla. The Variational Ising Classifier (VIC) algorithm for coherently contaminated data. In Advances in Neural Information Processing Systems 17, pages 1497-1504. MIT Press, Cambridge, MA, 2005.
18. J. Winn and N. Jojic. LOCUS: Learning Object Classes with Unsupervised Segmentation. In ICCV, 2005.