Stable keypoint recognition using viewpoint generative learning

Takumi Yoshida, Hideo Saito, Masayoshi Shimizu, Akinori Taguchi

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

6 Citations (Scopus)

Abstract

We propose a stable keypoint recognition method that is robust to viewpoint changes. Conventional local features such as SIFT, SURF, etc., have scale and rotation invariance but often fail in matching points when the camera pose significantly changes. In order to solve this problem, we adopt viewpoint generative learning. By generating various patterns as seen from different viewpoints and collecting local invariant features, our system can learn feature descriptors under various camera poses for each keypoint before actual matching. Experimental results comparing usual local feature matching or patch classification method show both robustness and fastness of learning.

Original languageEnglish
Title of host publicationVISAPP 2013 - Proceedings of the International Conference on Computer Vision Theory and Applications
Pages310-315
Number of pages6
Publication statusPublished - 2013 May 31
Event8th International Conference on Computer Vision Theory and Applications, VISAPP 2013 - Barcelona, Spain
Duration: 2013 Feb 212013 Feb 24

Publication series

NameVISAPP 2013 - Proceedings of the International Conference on Computer Vision Theory and Applications
Volume2

Other

Other8th International Conference on Computer Vision Theory and Applications, VISAPP 2013
CountrySpain
CityBarcelona
Period13/2/2113/2/24

Keywords

  • Generative learning
  • Keypoint recognition
  • Local features
  • Pose estimation

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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  • Cite this

    Yoshida, T., Saito, H., Shimizu, M., & Taguchi, A. (2013). Stable keypoint recognition using viewpoint generative learning. In VISAPP 2013 - Proceedings of the International Conference on Computer Vision Theory and Applications (pp. 310-315). (VISAPP 2013 - Proceedings of the International Conference on Computer Vision Theory and Applications; Vol. 2).