Reliable matching of building facades using geometric measurements and an iterative reliability verification model

This paper presents an approach for reliable matching of feature points in images of building facades with wide viewpoint changes. These images of man-made facades can render state-of-the-art techniques such as SIFT ineffective due to low texture and inclusion of repetitive items. The proposed approach integrates both appearance-based measurements and geometric measurements to find correct feature matches. The reliabilities of feature matches are reinforced with a belief network based on the assumption that if a match is a true match, it should have similarity in geometry with the other matches. The geometric measurements used here are the distances and angles between the imaginary lines connecting the pairs of matches. The most reliable match which we call the reference match is selected based on the achievement of maximum votes from other matches. The number of correct matches is expanded by selecting the matches which have similar geometry with respect to the reference match. A homography model is then applied to eliminate the wrongly selected matches. Extensive experimental results demonstrate higher reliability of our approach than state-of-the-art techniques such as SIFT, and SIFT in combination with RANSAC.