3D object recognition using effective features selected by evaluating performance of discrimination

Shoichi Takei, Shuichi Akizuki, Manabu Hashimoto

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

We propose a reliable 3D position and pose recognition method for complicated scenes including randomly stacked objects. Conventional methods use a small number of features selected by analyzing a target object model for recognition. The small number contributes to high-speed recognition, but actually the features include both 'true' and 'false' features. True features exist only in the target object and not in other parts, so they are valid for correct recognition purposes. On the other hand, false features exist in both the target object and in other parts, such as contacting areas caused by multiple objects. As a result of their matching incorrect parts, misrecognition may occur. To solve this problem, we propose a new method that uses effective features selected by analyzing not only the target object but also contacting areas caused by multiple objects. For predicting contacting areas, we generated very real input scenes by using 3D Computer Graphics (3D-CG) techniques and a physics-based simulator. Features that have high discrimination performance in the feature space are selected and used for the matching process. The method is robust to disturbances such as feature variability and achieves high feature separability; these enable it to achieve good discrimination performance. The method achieves reliable and fast object recognition by using a small number of effective features that have high discrimination performance. Experimental results show that the method's recognition success rate is from 33.6% to 92.9% higher than that of the Vector Pair Matching (VPM) method proposed by Akizuki et al. and that its processing time is within 1.46 seconds.

本文言語English
ホスト出版物のタイトル2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014
出版社Institute of Electrical and Electronics Engineers Inc.
ページ70-75
ページ数6
ISBN(電子版)9781479951994
DOI
出版ステータスPublished - 2014
外部発表はい
イベント2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014 - Singapore, Singapore
継続期間: 2014 12 102014 12 12

出版物シリーズ

名前2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014

Other

Other2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014
国/地域Singapore
CitySingapore
Period14/12/1014/12/12

ASJC Scopus subject areas

  • コンピュータ ビジョンおよびパターン認識
  • 人間とコンピュータの相互作用
  • 人工知能
  • 制御およびシステム工学

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