摘要
选取Hu不变矩、手势轮廓的凹陷个数及其周长与面积比为手势识别的主要特征,采用了基于径向基核的SVM分类器进行0～9十种手势的识别。实验结果表明,在背景单一、光照情况良好条件下,该方法具有很高的识别率,并且简单快速。
This paper selects Hu moment invariants, the sag number of gestures outline and the ratio of its perimeter and area as the main characteristics of gesture recognition, adopts SVM classifier based on the RBF kernel to recognize ten kinds gestures of 0-9. The resuh of the experiment shows that this method has a high recognition rate under a single background and good illumination condition, and it is more simple and faster.
出处
《微型机与应用》
2012年第6期32-35,共4页
Microcomputer & Its Applications
关键词
手势识别
HU矩
支持向量机
gesture identification
Hu moments
support vector machine
