Stable keypoint recognition using viewpoint generative learning

Takumi Yoshida, Hideo Saito, Masayoshi Shimizu, Akinori Taguchi

研究成果: Conference contribution

6 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルVISAPP 2013 - Proceedings of the International Conference on Computer Vision Theory and Applications
ページ310-315
ページ数6
出版ステータスPublished - 2013 5 31
イベント8th International Conference on Computer Vision Theory and Applications, VISAPP 2013 - Barcelona, Spain
継続期間: 2013 2 212013 2 24

出版物シリーズ

名前VISAPP 2013 - Proceedings of the International Conference on Computer Vision Theory and Applications
2

Other

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

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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