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• W2027170820 abstract "In few years, min-cut/max-flow approach has become a leading method for solving a wide range of problems in computer vision. However, min-cut/max-flow approaches involve the construction of huge graphs which sometimes do not fit in memory. Currently, most of the max-flow algorithms are impracticable to solve such large scale problems. In this paper, we introduce a new strategy for reducing exactly graphs in the image segmentation context. During the creation of the graph, we test if the node is really useful to the max-flow computation. Numerical experiments validate the relevance of this technique to segment large scale images." @default.
• W2027170820 created "2016-06-24" @default.
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• W2027170820 date "2011-08-01" @default.
• W2027170820 modified "2023-09-29" @default.
• W2027170820 title "Reduced graphs for min-cut/max-flow approaches in image segmentation" @default.
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• W2027170820 doi "https://doi.org/10.1016/j.endm.2011.05.012" @default.
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