Contour Matching Using Local Affine Transformations.

摘要

Visual processing tasks often require the matching of contours in two images. Examples include determining image motion and matching features for object recognition. We propose a scheme that takes partial constraints on the matching between contours in two images and finds the matches between these contours using local affine transformations. This new scheme is motivated in part by existing ideas for both recovering optical flow and matching features for object recognition, and in part by short-term motion processing in the primate visual system. The scheme assumes that contours are locally approximated by orthographic projections of planar objects. A local affine transformation is determined for each contour point using oriented elliptical Gaussian neighborhoods that smoothly integrate information over proximally connected contours at several spatial scales. At the largest scale satisfying available constraints a minimal solution mechanism employs a modified pseudoinverse to directly predict matches that are closest to the simplest purely translational correspondence. Using this scheme, the matches for points along contours can be determined in parallel without iteration. The scheme's matching performance is assessed by simulation on noisy synthetic and natural contour imagery.... Matching, Aperture problem, Optical flow, Affine transformation, Correspondence alignment-based recognition.