Motion statistic based local homography transformation estimation for mismatch removal

Songlin Du, Takeshi Ikenaga

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Accurately establishing pixel-level correspondence between images taken from same objects is an essential problem in many computer vision applications, such as 3D reconstruction, simultaneous localization and mapping (SLAM), and augmented reality (AR). Existing local feature descriptor based image matching approaches are unable to avoid mismatches which cause negative effects to the above mentioned applications. This paper proposes a motion statistic based local homography transformation estimation method for removing mismatches. The proposed method estimates local homography transformations between the grids in a pair of images and then classifies each match as correct or incorrect by checking whether it is consisting with the corresponding local homography transformation or not. Experimental results on the widely used Oxford affine image dataset show that the proposed approach finds out more potential correct matches than the existing state-of-the-art method.

Original languageEnglish
Title of host publicationAIVR 2019 - 2019 3rd International Conference on Artificial Intelligence and Virtual Reality
PublisherAssociation for Computing Machinery
Pages47-50
Number of pages4
ISBN (Electronic)9781450371612
DOIs
Publication statusPublished - 2019 Jul 27
Event3rd International Conference on Artificial Intelligence and Virtual Reality, AIVR 2019 - Singapore, Singapore
Duration: 2019 Jul 272019 Jul 29

Publication series

NameACM International Conference Proceeding Series

Conference

Conference3rd International Conference on Artificial Intelligence and Virtual Reality, AIVR 2019
CountrySingapore
CitySingapore
Period19/7/2719/7/29

Keywords

  • Image matching
  • Local feature descriptor
  • Local homography transformation
  • Mismatch removal

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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