Hand gesture recognition using a neural network shape fitting technique

被引：191

作者：

Stergiopoulou, E. ^{[1
]}

Papamarkos, N. ^{[1
]}

机构：

[1] Democritus Univ Thrace, Dept Elect & Comp Engn, Image Proc & Multimedia Lab, GR-67100 Xanthi, Greece

来源：

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE | 2009年 / 22卷 / 08期

关键词：

Human-machine interaction; Hand gesture; Skin color detection; YCbCr color space; Neural network; SYSTEM; FACE;

D O I：

10.1016/j.engappai.2009.03.008

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

080201 [机械制造及其自动化];

摘要：

A new method for hand gesture recognition that is based on a hand gesture fitting procedure via a new Self-Growing and Self-Organized Neural Gas (SGONG) network is proposed. initially, the region of the hand is detected by applying a color segmentation technique based on a skin color filtering procedure in the YCbCr color space. Then, the SGONG network is applied on the hand area so as to approach its shape. Based on the output grid of neurons produced by the neural network, palm morphologic characteristics are extracted. These characteristics, in accordance with powerful finger features, allow the identification of the raised fingers. Finally, the hand gesture recognition is accomplished through a likelihood-based classification technique. The proposed system has been extensively tested with success. (C) 2009 Elsevier Ltd. All rights reserved.

引用

页码：1141 / 1158

页数：18

共 28 条

[1]

Albiol A, 2001, IEEE IMAGE PROC, P122, DOI 10.1109/ICIP.2001.958968

[2]

ATSALAKIS A, 2005, CIARP, P977

[3]

ATSALAKIS A, 2005, VVG05, P53

[4]

Color reduction and estimation of the number of dominant colors by using a self-growing and self-organized neural gas [J].