融合图像轮廓矩和傅立叶描叙子的手势识别

Fusion of Image Contour Moments and Fourier Descriptors for the Hand Gesture Recognition

针对手势识别中人手是复杂变形体,手部特征描叙容易受到环境因素影响的特点,提出了一种基于混合轮廓特征的手势识别的新方法.首先根据手的颜色信息将手从复杂的背景中分离出来,然后提取出手轮廓的矩不变量和Fourier描叙子特征,然后将2种特征融合构成混合特征,最后将新特征输入BP神经网络进行识别.实验表明:此方法具有较好的鲁棒性和较高的识别率.

Hand is a highly variable organ and hand features are easily affected by environmental factors. Considering the characteristics of hand gesture, a novel hand gesture recognition algorithm based on fusion contour moments is presented. First, According to the color cue the hand is available to extract from the complicated background, then the contour moment invariant and Fourier Descriptor are extracted and fused into a mixed feature, finally the mixed feature are put into BP network to identity. The experimental results show that the method proposed has better ro- bustness and higher recognition rate.