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<p><b>Abstract</b>—The problem of matching two planar sets of points in the presence of geometric uncertainty has important applications in pattern recognition, image understanding, and robotics. The first set of points corresponds to the "template." The other set corresponds to the "image" that—possibly—contains one or more deformed versions of the "template" embedded in a cluttered image. Significant progress has been made on this problem and various polynomial-time algorithms have been proposed. In this article, we show how to sample the "image" in linear time, reducing the number of foreground points <it>n</it> by a factor of two-six (for commonly occurring images) without degrading the quality of the matching results. The direct consequence is a time-saving by a factor of 2<super><it>p</it></super>−6<super><it>p</it></super> for an <it>O</it>(<it>n</it><super><it>p</it></super>) matching algorithm. Our result applies to a fairly large class of available matching algorithms.</p>
Sampling, model-based vision, matching under uncertainty, approximate matching, image understanding.

M. Akra, L. Bazzi and S. Mitter, "Sampling of Images for Efficient Model-Based Vision," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 21, no. , pp. 4-11, 1999.
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