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.