Epipolar Geometry Estimation by Tensor Voting in 8D

We present a novel, efficient, initializationfree approach to the problem of epipolar geometry estimation, by formulating it as one of hyperplane inference from a sparse and noisy point set in an 8D space. Given a set of noisy point correspondences in twoimagesas obtainedfromtwoviews of astaticscene without correspondences, even in the presence of moving objects, our method pulls out inlier matches while rejecting outliers. Unlike most methods which optimize certain objective function, our approach does not involve initialization or any search in the parameter space, and therefore is free of the problem of local optima or poor convergence. Since no search is involved, it is unnecessary to impose simplifying assumption (such as affine camera or local planar homography) to the scene being analyzed for reducing the search complexity. Subject to the general epipolar constraint only, we detect wrong matches by establishing salient "extremalities" via a novel approach, 8D Tensor V...

• Type: Conference
• Year: 1999
• Where: ICCV
• Authors: Chi-Keung Tang, Gérard G. Medioni, Mi-Suen Lee
• Keywords: Computer Vision, ICCV 1999, Noisy Point, Point Set, Wrong Matches