Classification of Hand Postures Based on 3D Vision Model for Human-Robot Interaction

被引：0

作者：

Takimoto, Hironori ^{[1
]}

Yoshimori, Seiki ^{[2
]}

Mitsukura, Yasue ^{[3
]}

Fukumi, Minoru ^{[4
]}

机构：

[1] Okayama Prefectural Univ, Fac Comp Sci & Syst Engn Commun & Engn, 111 Kuboki, Okayama 7191197, Japan

[2] Nippon Bunri Univ, Fac Engn, Oita 8700397, Japan

[3] Tokyo Univ Agr & Technol, Grad Sch BioAppl & Syst Engn, Tokyo 1848588, Japan

[4] Univ Tokushima, Dept Informat & Sci Intelligent Syst, Tokushima 7708506, Japan

来源：

2010 IEEE RO-MAN | 2010年

关键词：

RECOGNITION;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, a method for hand posture recognition, which is robust for hand posture changing in an actual environment, is proposed. Conventionally, a data glove device and a 3D scanner have been used for the feature extraction of hand shape. However, the performance of each approach is affected by hand posture changing. Therefore, this paper proposes the posture fluctuation model for efficient hand posture recognition, based on 3D hand shape and color feature obtained from a stereo camera. A large set of dictionary for posture recognition is built by various leaned hand images which were auto-created from one scanned hand image, based on plural proposed models. In order to show the effectiveness of proposed method, performance and processing times for posture recognition are compared to conventional method. In addition, we perform the evaluation experiment by using the Japanese sign language.

引用

页码：292 / 297

页数：6

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