Graph cuts in computer vision: Difference between revisions

Revision as of 12:33, 9 April 2007

Revision as of 19:42, 11 April 2007

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Prior to this result ''approximate'', although more general techniques such as [[simulated annealing]], as proposed by the Geman brothers, or iterated conditional modes, a type of [[greedy algorithm]] as suggested by Julian Besag, were used to solve these types of problems. See the references below.

Although the general <math>k</math>-colour problem remains unsolved for <math>k > 2,</math> the approach of Greig, Porteous and Seheult has turned out to have wide applicability in general computer vision problems. TheirSee Boykov, Veksler and Zabih. The Greig, Porteous and Seheult approach is often applied iteratively to a sequence of binary problems, usually yielding near optimal solutions. See the article by Funka-Lea at al, as referenced below, for a recent application.

== References ==

*J.E. Besag (1986), ''On the statistical analysis of dirty pictures (with discussion)'', Journal of the Royal Statistical Society Series B, '''48''', 259 - 302.

* Y. Boykov, O. Veksler and R. Zabih (2001), "Faxt approximate energy minimisation via graph cuts", IEEE Transactions on Pattern Analysis and Machine Intelligence, '''29''', 1222 - 1239.

*G. Funka-Lea, Y. Boykov, C. Florin, M. P. Jolly, R. Moreau-Gobard, R. Ramaraj and D. Rinck (2006), "Automatic heart isolation for CT coronary visualization using graph cuts", IEEE International Symposium on Biomedical Imaging, 614 - 617.

*D. Geman and S. Geman (1984), ''Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images'', IEEE Trans. Pattn Anal. Mach. Intell., '''6''', 721 - 741.