Estimating the Number of Correct Matches Using Only Spatial Order.

Correctly matching feature points in a pair of images is an important preprocessing step for many computer vision applications. In this paper we propose an efficient method for estimating the number of correct matches without explicitly computing them. To this end, we propose to analyze the set of matches using the spatial order of the features, as projected to the $x$ x -axis of the image. The set of features in each image is thus represented by a sequence, and analyzed using the Kendall and Spearman Footrule distance metrics between permutations. This result is interesting in its own right. Moreover, we demonstrate three useful applications of our method: (i) a new halting condition for RANSAC based epipolar geometry estimation methods, (ii) discarding spatially unrelated image pairs in the Structure-from-Motion pipeline, and (iii) computing the probability that a given match is correct based on the rank of the features within the sequences. Our experiments on a large number of synthetic and real data demonstrate the effectiveness of our method. For example, the running time of the image matching stage in the Structure-from-Motion pipeline may be reduced by about 90 percent while preserving about 85 percent of the image pairs with spatial overlap. [ABSTRACT FROM AUTHOR]