Modelling composite shapes by gibbs random fields

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

Volumn , Issue , 2011, Pages 2177-2182

  Flach, Boris ^a   Schlesinger, Dmitrij ^b  

^a CZECH TECHNICAL UNIVERSITY IN PRAGUE   (Czech Republic)

^b DRESDEN UNIVERSITY OF TECHNOLOGY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SCIENCE; COMPUTERS; ELECTRICAL ENGINEERING; SOFTWARE ENGINEERING;

COMPLEX SHAPES; EXPRESSIVE POWER; GIBBS RANDOM FIELD; SECOND ORDERS; SHAPE PRIORS; SPATIAL COMPOSITION; SPATIAL RELATIONS;

PATTERN RECOGNITION;

EID: 80052870294     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2011.5995726     Document Type: Conference Paper

References (13)

1. D. Cremers, N. Sochen, and C. Schnörr. A multiphase dynamic labeling model for variational recognition-driven image segmentation. IJCV, 66(1):67-81, January 2006. 2177
2. B. Flach and D. Schlesinger. Combining shape priors and MRF-segmentation. In N. da Vitoria Lobo et al., editor, S+SSPR 2008, pages 177-186. Springer, 2008. 2177
3. G. L. Gimel'farb. Texture modeling by multiple pairwise pixel interactions. IEEE Trans. Pattern Anal. Mach. Intell., 18(11):1110-1114, 1996. 2179
4. G. E. Hinton. Training products of experts by minimizing contrastive divergence. Neural Computation, 14(8):1771-1800, 2002. 2177
5. D. D. Hoffman. Visual Intelligence: How We Create What We See. W. W. Norton & Company, 2000. 2177
6. L. Ladicky, C. Russell, P. Kohli, and P. H. Torr. Associative hierarchical CRFs for object class image segmentation. In Proceedings IEEE 12. International Conference on Computer Vision, 2009. 2177
7. H. Liu, W. Liu, and L. J. Latecki. Convex shape decomposition. In CVPR, pages 97-104, 2010. 2177
8. S. Ramalingam, P. Kohli, K. Alahari, and P. Torr. Exact inference in multi-label CRFs with higher order cliques. In CVPR 2008, pages 1-8, June 2008. 2177
9. S. Roth and M. J. Black. Fields of experts. International Journal of Computer Vision, 82(2):205-229, 2009. 2177
10. A. D. Sokal. Monte Carlo methods in statistical mechanics: Foundations and new algorithms. Lectures notes, 1989. 2178
11. M. J. Wainwright and M. I. Jordan. Graphical models, exponential families, and variational inference. Foundations and Trends in Machine Learning, 1(1-2):1-305, 2008. 2178
12. C.-N. J. Yu and T. Joachims. Learning structural SVMs with latent variables. In International Conference on Machine Learning (ICML), 2009. 2178
13. A. Zalesny and L. V. Gool. Multiview texture models. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2001, pages 615-622. IEEE Computer Society, 2001. 2179