Estimating the Number of Correct Matches Using only Spatial Order

Lior Talker, Yael Moses, Ilan Shimshoni

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number8458181
Pages (from-to)2846-2860
Number of pages15
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume41
Issue number12
DOIs
StatePublished - 1 Dec 2019

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

  • Feature matching
  • RANSAC
  • correct matches
  • spatial order

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Applied Mathematics
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics

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