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- Graph_cuts_in_computer_vision abstract "As applied in the field of computer vision, graph cuts can be employed to efficiently solve a wide variety of low-level computer vision problems (early vision), 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 reduced to instances of the maximum flow problem in a 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 maximum a posteriori estimate of a solution. Although many computer vision algorithms involve cutting a graph (e.g., normalized cuts), the term "graph cuts" is applied specifically to those models which employ a max-flow/min-cut optimization (other graph cutting algorithms may be considered as graph partitioning algorithms)."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.".
- Graph_cuts_in_computer_vision wikiPageExternalLink gridcut.com.
- Graph_cuts_in_computer_vision wikiPageExternalLink software.html.
- Graph_cuts_in_computer_vision wikiPageExternalLink code.
- Graph_cuts_in_computer_vision wikiPageExternalLink MalikBLS.pdf.
- Graph_cuts_in_computer_vision wikiPageExternalLink EMMCVPR_paper.pdf.
- Graph_cuts_in_computer_vision wikiPageID "10531718".
- Graph_cuts_in_computer_vision wikiPageRevisionID "572171898".
- Graph_cuts_in_computer_vision hasPhotoCollection Graph_cuts_in_computer_vision.
- Graph_cuts_in_computer_vision subject Category:Bayesian_statistics.
- Graph_cuts_in_computer_vision subject Category:Computational_problems_in_graph_theory.
- Graph_cuts_in_computer_vision subject Category:Computer_vision.
- Graph_cuts_in_computer_vision type Abstraction100002137.
- Graph_cuts_in_computer_vision type Attribute100024264.
- Graph_cuts_in_computer_vision type ComputationalProblemsInGraphTheory.
- Graph_cuts_in_computer_vision type Condition113920835.
- Graph_cuts_in_computer_vision type Difficulty114408086.
- Graph_cuts_in_computer_vision type Problem114410605.
- Graph_cuts_in_computer_vision type State100024720.
- Graph_cuts_in_computer_vision comment "As applied in the field of computer vision, graph cuts can be employed to efficiently solve a wide variety of low-level computer vision problems (early vision), 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 reduced to instances of the maximum flow problem in a graph (and thus, by the max-flow min-cut theorem, define a minimal cut of the graph).".
- Graph_cuts_in_computer_vision label "Cortes de grafos en la visión por computador".
- Graph_cuts_in_computer_vision label "Graph cuts in computer vision".
- Graph_cuts_in_computer_vision sameAs Cortes_de_grafos_en_la_visión_por_computador.
- Graph_cuts_in_computer_vision sameAs m.02qgytf.
- Graph_cuts_in_computer_vision sameAs Q3774432.
- Graph_cuts_in_computer_vision sameAs Q3774432.
- Graph_cuts_in_computer_vision sameAs Graph_cuts_in_computer_vision.
- Graph_cuts_in_computer_vision wasDerivedFrom Graph_cuts_in_computer_vision?oldid=572171898.
- Graph_cuts_in_computer_vision isPrimaryTopicOf Graph_cuts_in_computer_vision.