Affordance-based 3D feature for generic object recognition

M. Iizuka, S. Akizuki, M. Hashimoto

研究成果: Conference contribution

抄録

Techniques for generic object recognition, which targets everyday objects such as cups and spoons, and techniques for approach vector estimation (e.g. estimating grasp position), which are needed for carrying out tasks involving everyday objects, are considered necessary for the perceptual system of service robots. In this research, we design feature for generic object recognition so they can also be applied to approach vector estimation. To carry out tasks involving everyday objects, estimating the function of the target object is critical. Also, as the function of holding liquid is found in all cups, so a function is shared in each type (class) of everyday objects. We thus propose a generic object recognition method that can estimate the approach vector by expressing an object's function as feature. In a test of the generic object recognition of everyday objects, we confirmed that our proposed method had a 92% recognition rate. This rate was 11% higher than the mainstream generic object recognition technique of using convolutional neural network (CNN).

本文言語English
ホスト出版物のタイトルThirteenth International Conference on Quality Control by Artificial Vision 2017
編集者Atsushi Yamashita, Hajime Nagahara, Kazunori Umeda
出版社SPIE
ISBN(電子版)9781510611214
DOI
出版ステータスPublished - 2017
外部発表はい
イベント13th International Conference on Quality Control by Artificial Vision, QCAV 2017 - Tokyo, Japan
継続期間: 2017 5月 142017 5月 16

出版物シリーズ

名前Proceedings of SPIE - The International Society for Optical Engineering
10338
ISSN(印刷版)0277-786X
ISSN(電子版)1996-756X

Other

Other13th International Conference on Quality Control by Artificial Vision, QCAV 2017
国/地域Japan
CityTokyo
Period17/5/1417/5/16

ASJC Scopus subject areas

  • 電子材料、光学材料、および磁性材料
  • 凝縮系物理学
  • コンピュータ サイエンスの応用
  • 応用数学
  • 電子工学および電気工学

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