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Authors: Takumi Yoshida 1 ; Hideo Saito 1 ; Masayoshi Shimizu 2 and Akinori Taguchi 2

Affiliations: 1 Keio University, Japan ; 2 Fujitsu Laboratories, Japan

ISBN: 978-989-8565-48-8

Keyword(s): Generative Learning, Keypoint Recognition, Local Features, Pose Estimation.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Motion, Tracking and Stereo Vision ; Stereo Vision and Structure from Motion ; Tracking and Visual Navigation

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.

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Paper citation in several formats:
Yoshida, T.; Saito, H.; Shimizu, M. and Taguchi, A. (2013). Stable Keypoint Recognition using Viewpoint Generative Learning.In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-48-8, pages 310-315. DOI: 10.5220/0004295203100315

@conference{visapp13,
author={Takumi Yoshida. and Hideo Saito. and Masayoshi Shimizu. and Akinori Taguchi.},
title={Stable Keypoint Recognition using Viewpoint Generative Learning},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013)},
year={2013},
pages={310-315},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004295203100315},
isbn={978-989-8565-48-8},
}

TY - CONF

JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013)
TI - Stable Keypoint Recognition using Viewpoint Generative Learning
SN - 978-989-8565-48-8
AU - Yoshida, T.
AU - Saito, H.
AU - Shimizu, M.
AU - Taguchi, A.
PY - 2013
SP - 310
EP - 315
DO - 10.5220/0004295203100315

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