Image transport regression using mixture of experts and discrete markov random fields

2010 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010 - Proceedings

Volumn , Issue , 2010, Pages 1229-1232

  Michel, Fabrice ^a,b   Paragios, Nikos ^a,b  

^a Ecole Centrale de Paris   (France)

^b INRIA SACLAY   (France)

Author keywords

Discrete MRF; Image registration; Kernel methods; Machine learning; Mixture of experts

Indexed keywords

CLASSIFICATION (OF INFORMATION); COST FUNCTIONS; IMAGE REGISTRATION; IMAGE SEGMENTATION; LEARNING SYSTEMS; MEDICAL IMAGING; MIXTURES; OPTIMIZATION;

DISCRETE MRF; DISCRETE OPTIMIZATION; KERNEL METHODS; LINEAR COMBINATIONS; MARKOV RANDOM FIELDS; MIXTURE OF EXPERTS; MULTI LABEL CLASSIFICATION; STATISTICAL REGRESSION;

MARKOV PROCESSES;

EID: 77955182431     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISBI.2010.5490217     Document Type: Conference Paper

References (12)

1. B. Glocker, N. Komodakis, G. Tziritas, N. Navab, and N. Paragios, "Dense image registration through mrfs and efficient linear programming," MedIA, vol. 12, no. 6, pp. 731-741, 2008.
2. B. Zitová and J. Flusser, "Image registration methods: a survey," ImaVis, vol. 21, pp. 977-1000, 2003.
3. F. Maes, A. Collignon, D. Vandermeulen, G. Marchal, and P. Suetens, "Multimodality image registration by maximization of mutual information," IEEE-TMI, vol. 16, no. 2, pp. 187-198, 1997.
4. A. C. S. Chung, W. M. Wells III, A. Norbash, and W.E.L. Grimson, "Multi-modal image registration by minimizing kullback-leibler distance," in MICCAI. 2002, pp. 525-532, Springer.
5. D. Rueckert, MJ Clarkson, DLG Hill, and DJ Hawkes, "Nonrigid registration using higher-order mutual information," in Proceedings of SPIE, 2000, vol. 3979, p. 438.
6. D. Lee, M. Hofmann, F. Steinke, Y. Altun, N.D. Cahill, and B. Schlkopf, "Learning the similarity measure for multi-modal 3d image registration," in CVPR, 2009.
7. R. A. Jacobs, M. I. Jordan, S. J. Nowlan, and G. E. Hinton, "Adaptive mixtures of local experts," Neur. Comp., vol. 3, no. 1, pp. 79-87, 1991.
8. S. Geman and D. Geman, "Stochastic relaxation, gibbs distributions, and the bayesian restoration of images," IEEE TPAMI, vol. 6, pp. 721-741, 1984.
9. N. Komodakis, G. Tziritas, and N. Paragios, "Performance vs computational efficiency for optimizing single and dynamic mrfs: Setting the state of the art with primal-dual strategies," CVIU, vol. 112, no. 1, pp. 14-29, 2008.
10. Lei Xu, M. I. Jordan, and G. E. Hinton, "An alternative model for mixtures of experts," in NIPS, 1995, vol. 7, pp. 633-640.
11. Y. Boykov, O. Veksler, and R. Zabih, "Fast approximate energy minimization via graph cuts," IEEE T-PAMI, vol. 11, pp. 1222-1239, 2001.
12. V. Kolmogorov, "Convergent tree-reweighted message passing for energy minimization," IEEE T-PAMI, vol. 28, no. 10, pp. 1568, 2006.