Search results for: Chang-Tsun Li
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An approach to reducing the labeling cost of Markov random fields within an infinite label space
Signal Processing > 2001 > 81 > 3 > 609-620
This work proposes a novel idea, called SOIL, for reducing the computational complexity of the maximum a posteriori optimization problem using Markov random fields (MRFs). The local characteristics of MRFs are employed so that the searching in a virtually infinite label space is confined in a small finite space. Globally, the number of labels allowed is as many as the number of image sites while locally...
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