[1]余思泉,曹江涛,李平,等.基于空间金字塔特征包的手势识别算法[J].智能系统学报,2015,10(03):429-435.[doi:10.3969/j.issn.1673-4785.201405054]
 YU Siquan,CAO Jiangtao,LI Ping,et al.Hand gesture recognition based on the spatial pyramid bag of features[J].CAAI Transactions on Intelligent Systems,2015,10(03):429-435.[doi:10.3969/j.issn.1673-4785.201405054]
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基于空间金字塔特征包的手势识别算法(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第10卷
期数:
2015年03期
页码:
429-435
栏目:
出版日期:
2015-06-25

文章信息/Info

Title:
Hand gesture recognition based on the spatial pyramid bag of features
作者:
余思泉1 曹江涛1 李平1 姬晓飞2
1. 辽宁石油化工大学 信息与控制工程学院, 辽宁 抚顺 113001;
2. 沈阳航空航天大学 自动化学院, 辽宁 沈阳 110136
Author(s):
YU Siquan1 CAO Jiangtao1 LI Ping1 JI Xiaofei2
1. School of Information and Control Engineering, Liaoning Shihua University, Fushun 113001, China;
2. School of Automation, Shenyang Aerospace University, Shenyang 110136, China
关键词:
手势识别手势图像尺度不变特征变换空间金字塔特征包直方图相交核支持向量机
Keywords:
hand gesture recognitionhand gesture imagescale invariant feature transform (SIFT)spatial pyramidbag of featureshistogram intersection kernelsupport vector machines (SVM)
分类号:
TP319
DOI:
10.3969/j.issn.1673-4785.201405054
文献标志码:
A
摘要:
为了解决基于尺度不变特征变换的特征包(BoF-SIFT)算法在描述手势图像特征时对特征点分布情况无法确定的问题,提出了空间金字塔特征包算法提取手势图像特征.该算法通过构造图像金字塔改善了传统的BoF-SIFT算法,生成的描述子能有效表征手势图像的局部特征和全局特征,并能表示图像特征点的分布特性.采用直方图相交核支持向量机进行手势识别.在标准数据库上的测试表明,该算法对于10种手语得到了92.92%的正确识别率,验证了算法的有效性.
Abstract:
A novel algorithm based on the spatial pyramid bag of features is proposed to describe the hand image. It is proposed in order to solve the problem that the distribution of feature points cannot be ascertained when using the hand gesture descriptor based on bag of feature of scale invariant feature transform (BoF-SIFT). The capability of the BoF-SIFT can be improved by generating image spatial pyramid. The descriptor can effectively represent the posture by combining the global features and local features of the gesture image, as well as the distribution character of image feature points. Finally, the hand posture recognition is achieved by using the histogram intersection kernel support vector machine (SVM). The experiment on standard database demonstrates the average recognition rate can reach 92.92% for 10 kinds of gestures recognition, verifying the efficiency and effectiveness of the proposed algorithm.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2014-5-26;改回日期:。
基金项目:国家自然科学基金资助项目(61103123).
作者简介:余思泉,男,1988年生,硕士研究生,主要研究方向为图像处理、模式识别,申请发明专利2项. 曹江涛,男,1978年生,教授、博士,中国自动化学会机器人专业委员会委员和青工委委员,入选首批辽宁省高校杰出青年学者成长计划,主要研究方向为智能方法及其在工业控制和视频信息处理上的应用.承担国家自然科学基金等项目多项,发表学术论文40余篇,其中被SCI检索6篇、EI检索22篇.李平,男,1964年生,教授,博士生导师,IEEE高级会员,中国自动化学会过程控制专业委员会委员,入选辽宁省百千万人才工程百人层次,主要研究方向为工业过程的先进控制理论及其应用.承担国家“863”计划项目、国家自然科学基金等项目多项,发表学术论文100余篇,其中被SCI、EI检索50余篇.
通讯作者:曹江涛. E-mail: jtcao@lnpu.edu.cn.
更新日期/Last Update: 2015-07-15