Curvature feature based shape analysis

Yufeng Chen*, Fengxia Li, Tianyu Huang

*此作品的通讯作者

科研成果: 书/报告/会议事项章节会议稿件同行评审

2 引用 (Scopus)

摘要

A novel method is presented to evaluate the similarity of shapes based on the curvature features and their distribution. Firstly the curvature information is used to define the curvature features, which are learned and searched by the proposed statistic method. Secondly the structural features of each pair are measured, so that the distribution of the curvature feature can be further measured. Taking both advantages of the local shape context analysis and the global feature distances optimization, our method can endure large nonrigid distortion and occlusion. The experiments, which have been implemented on the MPEG-7 shape database, show that this method is efficient and robust under certain shape distortion. Another experiment on the abnormal behavior detection shows its potential in shape detection, motion tracking, image retrieving and the related areas.

源语言英语
主期刊名Advanced Intelligent Computing Theories and Applications
主期刊副标题With Aspects of Theoretical and Methodological Issues - 4th International Conference on Intelligent Computing, ICIC 2008, Proceedings
414-421
页数8
DOI
出版状态已出版 - 2008
活动4th International Conference on Intelligent Computing, ICIC 2008 - Shanghai, 中国
期限: 15 9月 200818 9月 2008

出版系列

姓名Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
5226 LNCS
ISSN(印刷版)0302-9743
ISSN(电子版)1611-3349

会议

会议4th International Conference on Intelligent Computing, ICIC 2008
国家/地区中国
Shanghai
时期15/09/0818/09/08

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引用此

Chen, Y., Li, F., & Huang, T. (2008). Curvature feature based shape analysis. 在 Advanced Intelligent Computing Theories and Applications: With Aspects of Theoretical and Methodological Issues - 4th International Conference on Intelligent Computing, ICIC 2008, Proceedings (页码 414-421). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 卷 5226 LNCS). https://doi.org/10.1007/978-3-540-87442-3_52