基于MEB-SVM的静态手势识别研究

作者：章丰明，任彧* 日期：2010-06-28/span> 浏览：3595 查看PDF文档

基于MEB-SVM的静态手势识别研究
章丰明，任彧*
（杭州电子科技大学 智能与软件研究所，浙江 杭州 310018）

摘要：为了实现快速高准确率的静态手势识别，提出了一种新的支持向量机分类方法。图像分割是图像识别的基本步骤，采用彩色直方图为特征可以将手部区域从背景中分离出来；然后将得到的手部图像去除手部以外的区域，并二值化，得到手的轮廓；再用8邻域方法处理轮廓图，得到一个表示轮廓的坐标序列，再将复数序列采用傅里叶变换，从而得到一个大小、平移、旋转不变的一维矢量。最后对矢量用MBE-SVM进行分类、识别。通过这种方法完成的手势识别正确率达到了90％以上，基本实现了静态手势识别的目标。
关键词：支持向量机;MBE-SVM;识别;分割
中图分类号：TH7；TP391.4文献标识码：A文章编号：1001-4551(2010)06-0120-04

Static gesture recognition research with MEB-SVM

ZHANG Feng-ming, Ren Yu
（Institute of Software and Intelligence, Hangzhou Dianzi University, Hangzhou 310018, China）

Abstract: Aiming at realizing high speed and high accuracy hand gesture recognition, a new support vector machine(abbreviated as SVM) classifier was proposed. Image segmentation is a first step of image recognition color-histogram method was applied to dig hand area from background; then binaries the hand image got from last step, and then, get the hand gestures contour line; after that 8-connected neighborhood method was used to deal with the contour line to get a serial coordinates denote the main information of hand gesture, then use Fourier transform to get a suitable vector. Finally MEB-SVM was used to classify these vectors and get the meaning of hand gestures. The result indicates that the MEB-SVM method can get an accuracy of 90％ in hand gesture recognition. And it can understand most hand gestures meaning.
Key words: support vector machine; MBE-SVM; recognition; segmentation