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The theory of [[Cut (graph theory)|graph cuts]], was first applied in [[Computer vision]] in the paper by Greig, Porteous and Seheult of Durham University, UK.
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, involving the introduction of a ''source'' and ''sink''. The problem was therefore converted into a NP hard problem which could be solved using known efficient [[algorithms]].
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.
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