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 disparate views of coplanar points and lines in a unified manner. The proposed method employs a randomized search strategy combined with the two-line two-point projective invariant 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.