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• W1878972690 abstract "Rapid advances in image acquisition and storage technology underline the need for algorithms that are capable of solving large scale image processing and computer-vision problems. The minimum cut problem plays an important role in processing many of these imaging problems such as, image and video segmentation, stereo vision, multi-view reconstruction and surface fitting. While several min-cut/max-flow algorithms can be found in the literature, their performance in practice has been studied primarily outside the scope of computer vision. We present here the results of a comprehensive computational study, in terms of execution times and memory utilization, of four recently published algorithms, which optimally solve the {em s-t} cut and maximum flow problems: (i) Goldberg's and Tarjan's {em Push-Relabel}; (ii) Hochbaum's {em pseudoflow}; (iii) Boykov's and Kolmogorov's {em augmenting paths}; and (iv) Goldberg's {em partial augment-relabel}. Our results demonstrate that the {em Hochbaum's pseudoflow} algorithm, is faster and utilizes less memory than the other algorithms on all problem instances investigated." @default.
• W1878972690 created "2016-06-24" @default.
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• W1878972690 date "2010-07-26" @default.
• W1878972690 modified "2023-09-27" @default.
• W1878972690 title "Competitive Analysis of Minimum-Cut Maximum Flow Algorithms in Vision Problems" @default.
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• W1878972690 hasPublicationYear "2010" @default.
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