[1]朱越,李振伟,杨晓利,等.基于视觉的静态手势识别系统[J].计算机技术与发展,2019,29(02):69-72.[doi:10．3969/j．issn．1673－629X．2019．02．014]
　ZHU Yue,LI Zhenwei,YANG Xiaoli,et al.Static Gesture Recognition System Based on Vision[J].,2019,29(02):69-72.[doi:10．3969/j．issn．1673－629X．2019．02．014]
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基于视觉的静态手势识别系统()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
29
期数:
2019年02期
页码:
69-72
栏目:
智能、算法、系统工程
出版日期:
2019-02-10

文章信息/Info

Title:
Static Gesture Recognition System Based on Vision
文章编号:
1673－629X(2019)02－0069－04
作者:
朱越; 李振伟; 杨晓利; 胡志刚
河南科技大学，河南 洛阳 471023
Author(s):
ZHU Yue; LI Zhen－wei; YANG Xiao－li; HU Zhi－gang
Henan University of Science and Technology，Luoyang 471023，China
关键词:
手势轮廓; 手势识别; 像素遍历; 图像处理; Matlab
Keywords:
gesture contour; gesture recognition; pixel traversal; image processing; Matlab
分类号:
TP302
DOI:
10．3969/j．issn．1673－629X．2019．02．014
摘要:
为了丰富手势识别方法的多样性，提高手势识别的正确率，提出了一种基于手势轮廓像素变化的手势识别方法。在 Matlab 环境下，设计并开发了一个基于视觉的静态手势识别系统。系统主要由两部分组成:手势分割与手势识别。该系统通过摄像头实时采集手势图像，根据 HSV 颜色空间上的聚簇特性，通过结合 ＲGB 和 HSV 双颜色空间的手势分割方法得到二值化图像，再对图像进行平滑滤波、形态学处理等处理后得到矩形手势区域图像。手势轮廓可以很好地表征手势图像的局部特征，文中在获取手势轮廓的基础上根据手势区域的像素变化进行手势识别。实验结果表明，该系统能够对规定的四种常用静态手势进行识别，系统鲁棒性很好，平均识别率达到了 90%，总体识别有效率达到了 85．9%，有较好的识别效果。
Abstract:
In order to enrich the diversity of gesture recognition method and improve the accuracy of gesture recognition，we propose a gesture recognition method based on the change of gesture contour pixel． In the Matlab environment，we design and develop a visual static gesture recognition system which consists of two parts:gesture segmentation and gesture recognition． This system collects the gesture images in real time through the camera． According to the clustering characteristics of HSV color space，the binary image is obtained by combining the gesture segmentation method of ＲGB and HSV dual－color space，and then the image is processed by smoothing and morphological processing． The gesture contour may well characterize the local feature of the gesture image，which is signed in response to the pixel change of the gesture region based on the acquired gesture contour． The experiment shows that the system can identify four commonly used static gestures with strong robustness and great recognition effect． The average recognition rate is up to 90% and the overall recognition rate is 85．9%．

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更新日期/Last Update: 2019-02-10