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Fast Approximate Energy Minimization via Graph Cuts

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The document presents algorithms for minimizing energy functions in early vision tasks using graph cuts. It introduces two main approaches: the α-β swap and α-expansion moves, which allow for large label changes while ensuring local minima are reached. The algorithms are designed to handle energy functions that involve pixel pairs and can be applied to various vision problems such as image restoration, stereo, and motion analysis. Experimental results demonstrate the efficiency of these methods compared to traditional simulated annealing techniques.

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