Hand posture recognition using hidden conditional random fields

Te Cheng Liu*, Ko Chih Wang, Augustine Tsai, Chieh-Chih Wang

*此作品的通信作者

研究成果: Conference contribution同行評審

5 引文 斯高帕斯(Scopus)

摘要

Body-language understanding is essential to human robot interaction, and hand posture recognition is one of the most important components in a body-language recognition system. The existing hand posture recognition approaches based on robust local features such as SIFT can be invariant to background noise and in-plane rotation. However the ignorance of the relationships among local features is a fundamental issue. The part-based models argue that objects of the same category share the same part-structure which consists of parts and relationships among parts. In this paper, a discriminative partbased model, Hidden Conditional Random Fields (HCRFs), is used to recognize hand postures. Although the existing global locations of features have been used to consider large scale dependency among parts in the HCRFs framework, the results are not invariant to in-plane rotation. New features by the distance to the image center are proposed to encode the global relationship as well as to perform in-plane rotationinvariant recognition. The experimental results demonstrate that the proposed approach is in-plane rotation-invariant and out performs the approach using Ada Boost with SIFT.

原文English
主出版物標題2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2009
頁面1828-1833
頁數6
DOIs
出版狀態Published - 4 十一月 2009
事件2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2009 - Singapore, Singapore
持續時間: 14 七月 200917 七月 2009

出版系列

名字IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM

Conference

Conference2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2009
國家/地區Singapore
城市Singapore
期間14/07/0917/07/09

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