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This document summarizes a talk on energy minimization for labeling problems with label costs and model fitting. It discusses standard MRF and CRF models used in computer vision, as well as adding label costs to these models. Optimization techniques like alpha expansion are used to approximate solutions. The document also discusses model fitting problems with an infinite number of labels using the PEARL framework of proposing labels, expanding, and reestimating labels. Applications discussed include image segmentation, geometric model fitting, and motion estimation.

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