Primitive shape recognition via superquadric representation using large margin nearest neighbor classifier

Ryo Hachiuma, Yuko Ozasa, Hideo Saito

研究成果: Conference contribution

抜粋

It is known that humans recognize objects using combinations and positional relations of primitive shapes. The first step of such recognition is to recognize 3D primitive shapes. In this paper, we propose a method for primitive shape recognition using superquadric parameters with a metric learning method, large margin nearest neighbor (LMNN). Superquadrics can represent various types of primitive shapes using a single equation with few parameters. These parameters are used as the feature vector of classification. The real objects of primitive shapes are used in our experiment, and the results show the effectiveness of using LMNN for recognition based on superquadrics. Compared to the previous methods, which used k-nearest neighbors (76.5%) and Support Vector Machines (73.5%), our LMNN method has the best performance (79.5%).

元の言語English
ホスト出版物のタイトルVISAPP
編集者Francisco Imai, Alain Tremeau, Jose Braz
出版者SciTePress
ページ325-332
ページ数8
ISBN(電子版)9789897582264
DOI
出版物ステータスPublished - 2017 1 1
イベント12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2017 - Porto, Portugal
継続期間: 2017 2 272017 3 1

出版物シリーズ

名前VISIGRAPP 2017 - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
5

Other

Other12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2017
Portugal
Porto
期間17/2/2717/3/1

    フィンガープリント

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

これを引用

Hachiuma, R., Ozasa, Y., & Saito, H. (2017). Primitive shape recognition via superquadric representation using large margin nearest neighbor classifier. : F. Imai, A. Tremeau, & J. Braz (版), VISAPP (pp. 325-332). (VISIGRAPP 2017 - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications; 巻数 5). SciTePress. https://doi.org/10.5220/0006153203250332