Statistical priors for efficient combinatorial optimization via graph cuts

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3953 LNCS, Issue , 2006, Pages 263-274

  Cremers, Daniel ^a   Grady, Leo ^b  

^a UNIVERSITY OF BONN   (Germany)

^b SIEMENS CORPORATE RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN INFERENCE; GRAPH CUTS; NOISY IMAGES; STATISTICALLY LEARNED CONSTRAINTS;

ALGORITHMS; COMBINATORIAL MATHEMATICS; COMPUTER VISION; MARKOV PROCESSES; OPTIMIZATION; SIMULATED ANNEALING; STATISTICAL METHODS;

GRAPH THEORY;

EID: 33745851543     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11744078_21     Document Type: Conference Paper

References (24)

1. K. Abend, T. Harley, and L. N. Kanal. Classification of binary random patterns. IEEE Transactions on Information Theory, 11:538-544, 1965.
2. A. Barbu and S.-C. Zhu. Generalizing Swendsen-Wang to sampling arbitrary posterior probabilities. IEEE Trans. on Patt. Anal. and Mach. Intell., 27(8): 1239-1253, 2005.
3. J. Besag. On the statistical analysis of dirty pictures. J. Roy. Statist. Soc., Ser. B., 48(3):259-302, 1986.
4. A. Blake and A. Zisserman. Visual Reconstruction. MIT Press, 1987.
5. Y. Boykov and V. Kolmogorov. An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans. on Patt. Anal. and Mach. Intell., 26(9): 1124-1137, 2004.
6. Y. Boykov, O. Veksler, and R. Zabih. Fast approximate energy minimization via graph cuts. IEEE Trans. on Patt. Anal. and Mach. Intell., 23(11): 1222-1239, 2001.
7. G.R. Cross and A.K. Jain. Markov random fields texture models. IEEE Trans. on Patt. Anal. and Mach. Intell., 5(1):25-39, 1983.
8. H. Derin and H. Elliott. Modeling and segmentation of noisy and textured images using Gibbs random fields. IEEE Trans. on Patt. Anal. and Mach. Intell., 9(1):39-55, Jan. 1987.
9. L. Ford and D. Fulkerson. Flows in Networks. Princeton University Press, Princeton, New Jersey, 1962.
10. W.T. Freeman, E. C. Pasztor, and O. T. Carmichael. Learning low-level vision. Int. J. of Computer Vision, 40(1):24-57, 2000.
11. S. Geman and D. Geman. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans. on Patt. Anal. and Mach. Intell. 6(6):721-741, 1984.
12. G. Gimelfarb. Texture modeling by multiple pairwise pixel interaction. IEEE Trans. on Patt. Anal. and Mach. Intell., 18(11):1110-1114, 1993.
13. D. M. Greig, B. T. Porteous, and A. H. Seheult. Exact maximum a posteriori estimation for binary images. J. Roy. Statist. Soc., Ser. B., 51(2):271-279, 1989.
14. H. Isnikawa. Exact optimization for Markov random fields with convex priors. IEEE Trans. on Patt. Anal. and Mach. Intell., 25(10): 1333-1336, Oct. 2003.
15. E. Ising. Beitrag zur Theorie des Ferromagnetismus. Zeitschrift für Physik, 23:253-258, 1925.
16. S. Kirkpatrick, C. D. Gelatt Jr., and M. P. Vecchi. Optimization by simulated annealing. Science, 220(4598):671-680, 1983.
17. V. Kolmogorov and R. Zabih. What energy functions can be minimized via graph cuts? IEEE Trans. on Patt. Anal. and Mach. Intell., 24(5):657-673, 2004.
18. B. Korte and J. Vygen. Combinatorial Optimization: Theory and Algorithms. Springer, 3rd edition, 2006.
19. S. Kumar and M. Hebert. Approximate parameter learning in discriminative fields. In Snowbird Learning Workshop, Utah, 2004.
20. B. S. Manjunath and R. Chellappa. Unsupervised texture segmentation using Markov random field models. IEEE Trans. on Patt. Anal. and Mach. Intell., 13(5):478-482, May 1991.
21. N. Metropolis, A. Rosenbluth, M. Rosenbluth, A. Teller, and E. Teller. Equation of state calculations by fast computing machines. J. Chem. Physics, 21:1087-1092, 1953.
22. J. C. Picard and H. D. Ratliff. Minimum cuts and related problems. Networks, 5:357-370, 1975.
23. W. Pieczynski, D. Benboudjema, and P. Lanchantin. Statistical image segmentation using triplet Markov fields. In Sebastiane B. Serpico, editor, SPIE Int. Symposium on Image and Signal Processing for Remote Sensing VIII, volume 4885, pages 92-101. SPIE, March 2003.
24. G. Winkler. Image Analysis, Random Fields and Markov Chain Monte Carlo Methods, volume 27 of Appl. of Mathematics. Springer, Heidelberg, 2003.