A GMS-Guided approach for 2D feature correspondence selection

Qunfang Tang, Jie Yang, Wenjing Jia, Xiangjian He, Qingnian Zhang, Haibo Liu

Research output: Journal PublicationArticlepeer-review

4 Citations (Scopus)

Abstract

Feature correspondence selection, which aims to seek as many true matches (i.e., inliers) as possible from a given putative set while minimizing false matches (i.e., outliers), is crucial to many feature-matching based tasks in computer vision. It remains a challenging problem how to deal with putative sets with low inlier ratios. To address this problem, in this paper, we propose a novel correspondence selection strategy, which is guided by Grid-based Motion Statistics (GMS). We first adopt the GMS to generate a small correspondence set with a high inlier ratio. Then, an accurate geometric model is built using the above correspondence set. Finally, the built geometric model is used to filter the given putative correspondence set to obtain true correspondences. The experimental results on benchmark datasets demonstrate that our proposed approach outperforms the state-of-the-art approaches for putative sets with various inlier ratios, especially for cases with low inlier ratios.

Original languageEnglish
Article number9006870
Pages (from-to)36919-36929
Number of pages11
JournalIEEE Access
Volume8
DOIs
Publication statusPublished - 2020
Externally publishedYes

Keywords

  • Correspondence selection
  • geometric model
  • grid-based motion statistics (GMS)
  • outlier

ASJC Scopus subject areas

  • Computer Science (all)
  • Materials Science (all)
  • Engineering (all)

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