%0 Journal Article
%T Optimal DAGSVM Based Posture Recognition for Human-robot Interaction<br>基于最优DAGSVM的服务机器人交互手势识别
%A QIAN Kun
%A MA Xudong
%A DAI Xianzhong
%A HU Chunhua
%A <br>钱 堃
%A 马旭东
%A 戴先中
%A 胡春华
%J 中国图象图形学报
%D 2009
%I 
%X A vision-based posture recognition system is proposed utilizing Optimal DAGSVM (Directed Acyclic Graph Support Vector Machine) classifier to achieve natural and reliable human-robot interactions. Coarse-to-fine feature detection scheme extracts skin-colored candidate regions, followed by face and hand verifications with Gabor filtered eye features and wavelet-moments of hand edge respectively. Statistical invariant moments and relative coordinates of face and hand regions are calculated as pattern feature vectors.A set of binary SVM classifiers are combined using Decision Directed Acyclic Graph with optimal structure to construct a more accurate multi-class DAGSVM classifier. Experimental result validates the reliable performance of the approach, where a natural and friendly interaction is achieved with a service robot.
%K posture recognition
%K wavelet-moments
%K directed acyclic graph support vector machine(DAGSVM)
%K human-robot interaction<br>手势识别
%K 小波矩
%K 有向无环图支持向量机
%K 人机交互
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=D06194629680C940ACE75262F54B9D85&aid=A6F21904F19397254C2451B76FF4DA32&yid=DE12191FBD62783C&vid=F3583C8E78166B9E&iid=CA4FD0336C81A37A&sid=7F5DDA4924737DF5&eid=2F56B21F91C9B05B&journal_id=1006-8961&journal_name=中国图象图形学报&referenced_num=0&reference_num=12