ReigSAC: fast discrimination of spurious keypoint correspondences on planar surfaces

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Various methods were proposed to detect/match special interest points (keypoints) in images and some of them (e.g., SIFT and SURF) are among the most cited techniques in computer vision research. This paper describes an algorithm to discriminate between genuine and spurious keypoint correspondences on planar surfaces. We draw random samples of the set of correspondences, from which homographies are obtained and their principal eigenvectors extracted. Density estimation on that feature space determines the most likely true transform. Such homography feeds a cost function that gives the goodness of each keypoint correspondence. Being similar to the well-known RANSAC strategy, the key finding is that the main eigenvector of the most (genuine) homographies tends to represent a similar direction. Hence, density estimation in the eigenspace dramatically reduces the number of transforms actually evaluated to obtain reliable estimations. Our experiments were performed on hard image data sets, and pointed that the proposed approach yields effectiveness similar to the RANSAC strategy, at significantly lower computational burden, in terms of the proportion between the number of homographies generated and those that are actually evaluated.