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As applied in the field of [[computer vision]], '''[[Cut (graph theory)|graph cuts]]''' can be employed to [[Polynomial time|efficiently]] solve a wide variety of low-level computer vision problems, such as image [[smoothing]], the stereo [[correspondence problem]], and many other computer vision problems that can be formulated in terms of [[energy minimization]]. Such energy minimization problems can be [[Reduction (complexity)|reduced]] to instances of the [[maximum flow problem]] in a [[Graph (mathematics)|graph]] (and thus, by the [[max-flow min-cut theorem]], define a minimal cut of the graph). Under most formulations of such problems in computer vision, the minimum energy solution corresponds to the [[MAP estimate|maximum a posteriori estimate]] of a solution.▼
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BRITTNEY SPEARS IN HOSPITAL:)
▲'''Bold text'''As applied in the field of [[computer vision]], '''[[Cut (graph theory)|graph cuts]]''' can be employed to [[Polynomial time|efficiently]] solve a wide variety of low-level computer vision problems, such as image [[smoothing]], the stereo [[correspondence problem]], and many other computer vision problems that can be formulated in terms of [[energy minimization]]. Such energy minimization problems can be [[Reduction (complexity)|reduced]] to instances of the [[maximum flow problem]] in a [[Graph (mathematics)|graph]] (and thus, by the [[max-flow min-cut theorem]], define a minimal cut of the graph). Under most formulations of such problems in computer vision, the minimum energy solution corresponds to the [[MAP estimate|maximum a posteriori estimate]] of a solution.
"Binary" problems (such as denoising a [[binary image]]) can be solved exactly using this approach; problems where pixels can be labeled with more than two different labels (such as stereo correspondence, or denoising of a [[grayscale]] image) cannot be solved exactly, but solutions produced are usually near the [[global optimum]].
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