Robust matching of images across large viewpoint changes

Abstract

This paper presents a novel approach to finding point correspondences between images of building facades with wide viewpoint variations, and at the same time returning a large list of true matches between the images. Such images comprise repetitive and symmetric patterns, which render popular algorithms e.g. SIFT to be ineffective. Feature descriptors such as SIFT that are based on region patches are also unstable under large viewing angle variations. In this paper, we integrate both the appearance and geometric properties of an image to find unique matches. First, we extract hypotheses of building facades based on a robust line fitting algorithm. Each hypothesis is defined by a planar convex quadrilateral in the image. which we call a 'q-region', and the four corners of each q-region provide the inputs from which a projective transformation model is derived. Next, a set of interest points are extracted from the images and are used to evaluate the correctness of the transformation model. The transformation model with the largest set of of matched interest points is selected as the correct model, and this model also returns the best pair of corresponding q-regions and the most number of point correspondences in the two images. Extensive experimental results demonstrate the robustness of our approach in which we achieve a tenfold increase in true matches when compared to state of the art techniques such as SIFT and MSER.