Wald’s Sequential Analysis for Time-constrained Vision Problems
Typ dokumentupříspěvek z konference - elektronický
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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 decisionmaking and derive quasi-optimal time-constrained solutions for three vision problems. The methodology is applied to face and interest point detection and to the RANSAC robust estimator. Error rates of the face detector proposed algorithm are comparable to the state-of-the-art methods. In the interest point application, the output of the Hessian-Laplace detector  is approximated by a sequential WaldBoost classifier which is about five times faster than the original with comparable repeatability. A sequential strategy based on Wald’s SPRT for evaluation of model quality in RANSAC leads to significant speed-up in geometric matching problems.
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