DocumentCode :
352924
Title :
Parametrized SOMs for hand posture reconstruction
Author :
Nölker, Claudia ; Ritter, Helge
Author_Institution :
Fac. of Technol., Bielefeld Univ., Germany
Volume :
4
fYear :
2000
fDate :
2000
Firstpage :
139
Abstract :
This paper describes the use of neural network for gesture recognition based on finger tips, a system that recognizes continuous hand postures from video images. Our approach yields a full identification of all finger joint angles. This allows a full reconstruction of the 3D hand shape, using an artificial hand model with 16 segments and 20 joint angles. The focus of the present paper is how to employ a parametrised SOM neural network for the inverse kinematics task to compute the angles of a hand model out of 3D positions of the fingertips. We show that this type of neural net does not only achieve excellent results from very few training examples, but also can be applied to uncommon data structures
Keywords :
computer vision; gesture recognition; image reconstruction; kinematics; self-organising feature maps; 3D hand model; finger tips; gesture recognition; hand posture reconstruction; inverse kinematics; neural network; parametrised SOM; self organising map; video images; Artificial neural networks; Computer networks; Fingers; Focusing; Image recognition; Image reconstruction; Image segmentation; Kinematics; Neural networks; Shape;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location :
Como
ISSN :
1098-7576
Print_ISBN :
0-7695-0619-4
Type :
conf
DOI :
10.1109/IJCNN.2000.860763
Filename :
860763
Link To Document :
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