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In detection and matching problems in computer vision, both classification errors and time to decision characterize the quality of an algorithmic solution. We show how to formalize such problems in the framework of sequential decision- making and derive quasi-optimal time-constrained solutions for a number of vision problems. Successful applications range from a state-of-the art matching algorithm to an order of magnitude speed-up of commonly used interest point detection.
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