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.
|Number of pages||15|
|Journal||IEEE Transactions on Pattern Analysis and Machine Intelligence|
|State||Published - 1 Dec 2019|
Bibliographical noteFunding Information:
This work was partially supported by the Israel Science Foundation, grant no. 930/12, by the Israeli Ministry of Science, grant no. 3-8744, and by the Israeli Innovation Authority in the Ministry of Economy and Industry.
© 2018 IEEE.
- Feature matching
- correct matches
- spatial order
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Computational Theory and Mathematics
- Artificial Intelligence
- Applied Mathematics