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== History ==
The theory of [[Cut (graph theory)|graph cuts]] used as [[graph cut optimization|an optimization method]] was first applied in [[computer vision]] in the seminal paper by Greig, Porteous and Seheult<ref name="D.M. Greig, B.T 1989">D.M. Greig, B.T. Porteous and A.H. Seheult (1989), ''[https://classes.cs.uchicago.edu/archive/2006/fall/35040-1/gps.pdf Exact maximum a posteriori estimation for binary images]'', Journal of the Royal Statistical Society, Series B, '''51''', 271–279.</ref> of [[Durham University|Durham University.]] Seheult and Porteous were members of Durham's much lauded small statistics group of the time, lead by [[Julian Besag]] and [[Peter Green (statistician)]], with the optimisation expert Margaret Greig also notable as first ever female member of staff of the Mathematical Sciences Department.
In the [[Bayesian statistics|Bayesian]] statistical context of [[smoothing]] noisy (or corrupted) images, they showed how the [[MAP estimate|maximum a posteriori estimate]] of a [[binary image]] can be obtained ''exactly'' by maximizing the [[Flow network|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 [[Donald Geman|Geman brothers]]<ref>D. Geman and S. Geman (1984), ''[http://www.dam.brown.edu/people/documents/stochasticrelaxation.pdf Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images]'', IEEE Trans. Pattern Anal. Mach. Intell., '''6''', 721–741.</ref>), or [[iterated conditional modes]] (a type of [[greedy algorithm]] as suggested by [[Julian Besag]])<ref>J.E. Besag (1986), ''On the statistical analysis of dirty pictures (with discussion)'', [[Journal of the Royal Statistical Society]] Series B, '''48''', 259–302</ref> were used to solve such image smoothing problems.
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