3D Gesture Recognition with Growing Neural Gas

被引：0

作者：

Serra-Perez, J. A. ^{[1
]}

Garcia-Rodriguez, J. ^{[1
]}

Orts-Escolano, S. ^{[1
]}

Garcia-Chamizo, J. M. ^{[1
]}

Montoyo-Bojo, J. ^{[1
]}

Angelopoulou, A. ^{[2
]}

Psarrou, A. ^{[2
]}

Mentzeopoulos, M. ^{[2
]}

Lewis, A. ^{[3
]}

机构：

[1] Univ Alicante, Dept Comp Technol, Alicante 03080, Spain

[2] Univ Westminster, Sch Comp Sci, Comp Vis & Imaging Res Grp, London W1W 6UW, England

[3] Griffith Univ, Sch Informat & Commun Technol, Brisbane, Qld 4111, Australia

来源：

2013 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2013年

关键词：

VECTOR QUANTIZATION;

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：

We propose the design of a real-time system to recognize and interpret hand gestures. The acquisition devices are low cost 3D sensors. 3D hand pose segmentation, characterization and tracking will be implemented using the growing neural gas (GNG) structure. The capacity of the system to obtain information with a high degree of freedom allows the encoding of many gestures and a very accurate motion capture. The use of hand pose models combined with motion information provided with GNG permits to deal with the problem of the hand motion representation. A natural interface applied to a virtual mirror writing system and a module to estimate hand pose have been designed to demonstrate the validity of the system.

引用

页数：8

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