Matching disparate views of planar surfaces using projective invariants

Feature matching is a prerequisite to a wide variety of vision tasks. This paper presents a method that addresses the problem of matching two views of coplanar points and lines in a unified manner. The views to be matched are assumed to have been acquired from disparate, i.e. very different viewpoints. By employing a randomized search strategy combined with the two-line two-point projective invariant, the proposed method is able to derive small sets of possibly matching points and lines. These candidate matches are then verified by recovering the associated plane homography, which is further used to predict more matches. The resulting scheme is capable of successfully matching features extracted from views that differ considerably, even in the presence of large numbers of outlying features. Experimental results from the application of the method to indoor and aerial images indicate its effectiveness and robustness. (C) 2000 Elsevier Science B.V. All rights reserved.