基于Hu-GLCM的手势识别方法研究

针对手势识别过程中单一手势特征对手势描述的不足,提出了一种基于改进Hu矩和灰度共生矩阵GLCM的手势识别方法 Hu-GLCM。首先利用肤色模型对采集的图像分割出手势区域;其次采用数学形态学和多边形拟合的方法提取手势的单连通轮廓,利用改进Hu-GLCM算法提取手势的几何形状特征和纹理特征并建立模板数据库;最后通过扩展的Canberra距离对手势图像进行识别和分类。实验结果表明,该改进算法对7种手势的平均识别率达到95%以上,且计算速度快,能够满足实时性的需求。

A hand gesture recognition method based on Hu-GLCM model

Aiming at the shortcoming that a single gesture feature cannot describe the gesture well in hand gesture recognition, a hand gesture recognition method based on Hu moments and Gray Level Co occurrence Matrix (Hu GLCM) is proposed. Firstly the skin color model is used to segment the captured image to get the gesture area. Secondly, the single connected contour of the gesture is extracted by mathematical morphology and polygon fitting. The improved Hu GLCM method is used to extract the geometric shape and texture features of the gesture and establish a template database. Finally, the gesture image is identified and classified by the extended Canberra distance. The experimental results show that the improved algorithm has an average recognition rate of more than 95% on seven kinds of gestures, and the computing speed is fast, which can meet the real time requirements.