Distribution Fields for Low Level Vision – Erik Learned-Miller (University of Massachusetts, Amherst)
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Consider the following fundamental problem of low level vision: given a large image I an a patch J from another image, find the “best matching” location of the patch J to image I. We believe the solution to this problem can be significantly improved. A significantly better solution to this problem has the potential to improve a wide variety of low-level vision problems, such as backgrounding, tracking, medical image registration, optical flow, image stitching, and invariant feature definition. We introduce a set of techniques for solving this problem based upon a representation called distribution fields. Distribution fields are an attempt to take the best from a wide variety of low-level vision techniques including geometric blur (Berg), mixture of Gaussians backgrounding (Stauffer), SIFT (Lowe) and HoG (Dalal and Triggs), local color histograms, bilateral filtering, congealing (Learned-Miller) and many other techniques. We show how distribution fields solve this “patch” matching problem, and, in addition to finding the optimum match of patch J to image I with a high success rate, the algorithm produces, as a by-product, a very natural assessment of the quality of that match. We call this algorithm the “sharpening match”. Using the sharpening match for tracking yields an extremely simple but state-of-the-art tracker. We also discuss application of these techniques to background subtraction and other low level vision problems.