CODE: Coherence Based Decision Boundaries for Feature Correspondence

Wen Yan Lin, Fan Wang, Ming Ming Cheng, Sai Kit Yeung, Philip H.S. Torr, Minh N. Do, Jiangbo Lu

Research output: Contribution to journalArticlepeer-review

Abstract

A key challenge in feature correspondence is the difficulty in differentiating true and false matches at a local descriptor level. This forces adoption of strict similarity thresholds that discard many true matches. However, if analyzed at a global level, false matches are usually randomly scattered while true matches tend to be coherent (clustered around a few dominant motions), thus creating a coherence based separability constraint. This paper proposes a non-linear regression technique that can discover such a coherence based separability constraint from highly noisy matches and embed it into a correspondence likelihood model. Once computed, the model can filter the entire set of nearest neighbor matches (which typically contains over 90 percent false matches) for true matches. We integrate our technique into a full feature correspondence system which reliably generates large numbers of good quality correspondences over wide baselines where previous techniques provide few or no matches.

Original languageEnglish (US)
Pages (from-to)34-47
Number of pages14
JournalIEEE transactions on pattern analysis and machine intelligence
Volume40
Issue number1
DOIs
StatePublished - Jan 1 2018

ASJC Scopus subject areas

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

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