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 uni ed 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 veri ed 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 di er 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 e ectiveness and robustness.
Lourakis et al. REF find point and line feature correspondences on a common plane by using a randomized search strategy to find an initial set of point and line matches.
Matching Disparate Views of Planar Surfaces Using Projective Invariants
BMVC
Computer Science