Motion statistic based local homography transformation estimation for mismatch removal

Songlin Du, Takeshi Ikenaga

研究成果: Conference contribution

抜粋

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.

元の言語English
ホスト出版物のタイトルAIVR 2019 - 2019 3rd International Conference on Artificial Intelligence and Virtual Reality
出版者Association for Computing Machinery
ページ47-50
ページ数4
ISBN(電子版)9781450371612
DOI
出版物ステータスPublished - 2019 7 27
イベント3rd International Conference on Artificial Intelligence and Virtual Reality, AIVR 2019 - Singapore, Singapore
継続期間: 2019 7 272019 7 29

出版物シリーズ

名前ACM International Conference Proceeding Series

Conference

Conference3rd International Conference on Artificial Intelligence and Virtual Reality, AIVR 2019
Singapore
Singapore
期間19/7/2719/7/29

ASJC Scopus subject areas

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

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  • これを引用

    Du, S., & Ikenaga, T. (2019). Motion statistic based local homography transformation estimation for mismatch removal. : AIVR 2019 - 2019 3rd International Conference on Artificial Intelligence and Virtual Reality (pp. 47-50). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3348488.3348496