基于改进RCE和RBF神经网络的静态手势识别  

Static hand gesture recognition based on improved RCE neural network and RBF neural network

作　　者：谭昶[1] 肖南峰[1] 

机构地区：[1]华南理工大学计算机科学与工程学院,广州510006

出　　处：《计算机工程与应用》2011年第7期172-176,共5页Computer Engineering and Applications

基　　金：国家自然科学基金与中国民用航空总局联合资助项目No.60776816;广东省自然科学基金重点项目No.8251064101000005~~

摘　　要：针对手势识别的手区域分割、手势特征提取和手势分类的三个过程,提出了一种新的静态手势识别方法。改进了传统的RCE神经网络用于手区域的分割,具有更高的运行速度和更强的抗噪能力。依Freeman链码方向提取手的边缘到掌心的距离作为手势的特征向量。将上一步得到的手势特征向量作为RBF神经网络的输入,进行网络的训练和分类。实验验证了该方法的有效性和可行性,并用其实现了人和仿人机器人的剪刀石头布的猜拳游戏。Hand gesture recognition usually includes hand image segmentation,features extraction,and hand gestures classification.In this paper,a new method is proposed to deal with the three phases of static hand gesture recognition.The traditional RCE neural network is improved and it is applied to hand image segmentation.The improved RCE neural network is proved to has running fast and strong ability of anti-noise.Freeman chain code is used to extract the distance from hand edge to the centre of the palm as feature vectors.Those feature vectors are used as the input of RBF neural network.Experiment results show this method is efficient and feasible.A scissors-paper-stone game between human and humanoid robot is developed by using this method.

关 键 词：手势识别 RCE神经网络 FREEMAN链码 RBF神经网络 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]