Graph cuts in computer vision: Difference between revisions

Revision as of 08:28, 8 April 2007

Revision as of 08:30, 8 April 2007

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In the Bayesian statistical context of smoothing noisy, or corrupted, images, Greig, Porteous and Seheult showed how the maximum a posteriori estimate of a binary image can be obtained ''exactly'' by maximising the flow through an associated image network. Prior to this result ''approximate'', although more general, techniques such as simulated annealing, as proposed by the Geman brothers, or iterated conditional modes, as suggested by Julian Besag, were used to solve these types of problems. See the references below.

Although the general k-colour problem remains unsolved for k > 2, the approach of Greig, Porteous and Seheult is now used very widelycommonly used in computer vision problems, often by applying their approach iteratively to a sequence of binary images to yield near optimal solutions.