History
The theory of graph cuts was first applied in computer vision in the paper by Greig, Porteous and Seheult of Durham University. In the Bayesian statistical context of smoothing noisy (or corrupted) images, they showed how the maximum a posteriori estimate of a binary image can be obtained exactly by maximizing the flow through an associated image network, involving the introduction of a source and sink. The problem was therefore shown to be efficiently solvable. Prior to this result, approximate 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 such image smoothing problems.
Although the general -colour problem remains unsolved for the approach of Greig, Porteous and Seheult has turned out to have wide applicability in general computer vision problems. Greig, Porteous and Seheult approaches are often applied iteratively to a sequence of binary problems, usually yielding near optimal solutions; see the article by Funka-Lea et al. for a recent application.
Read more about this topic: Graph Cuts In Computer Vision
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