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      An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision.

      IEEE transactions on pattern analysis and machine intelligence

      Algorithms, Artificial Intelligence, Cluster Analysis, Energy Transfer, Image Enhancement, methods, Image Interpretation, Computer-Assisted, Imaging, Three-Dimensional, Information Storage and Retrieval, Pattern Recognition, Automated, Photogrammetry, Photography, Reproducibility of Results, Sensitivity and Specificity

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          After [15], [31], [19], [8], [25], [5], minimum cut/maximum flow algorithms on graphs emerged as an increasingly useful tool for exact or approximate energy minimization in low-level vision. The combinatorial optimization literature provides many min-cut/max-flow algorithms with different polynomial time complexity. Their practical efficiency, however, has to date been studied mainly outside the scope of computer vision. The goal of this paper is to provide an experimental comparison of the efficiency of min-cut/max flow algorithms for applications in vision. We compare the running times of several standard algorithms, as well as a new algorithm that we have recently developed. The algorithms we study include both Goldberg-Tarjan style "push-relabel" methods and algorithms based on Ford-Fulkerson style "augmenting paths." We benchmark these algorithms on a number of typical graphs in the contexts of image restoration, stereo, and segmentation. In many cases, our new algorithm works several times faster than any of the other methods, making near real-time performance possible. An implementation of our max-flow/min-cut algorithm is available upon request for research purposes.

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