[1]赵一丹,肖秦琨,郭鹏.多目标优化的静态手语识别算法研究[J].计算机技术与发展,2019,29(02):54-59.[doi:10．3969/j．issn．1673－629X．2019．02．011]
　ZHAO Yidan,XIAO Qinkun,GUO Peng.Research on Static Sign Language Recognition Algorithm Based on Multi－objective Optimization[J].,2019,29(02):54-59.[doi:10．3969/j．issn．1673－629X．2019．02．011]
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多目标优化的静态手语识别算法研究()
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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:
54-59
栏目:
智能、算法、系统工程
出版日期:
2019-02-10

文章信息/Info

Title:
Research on Static Sign Language Recognition Algorithm Based on Multi－objective Optimization
文章编号:
1673－629X(2019)02－0054－06
作者:
赵一丹; 肖秦琨; 郭鹏
西安工业大学 电子信息工程学院，陕西 西安 710021
Author(s):
ZHAO Yi－dan; XIAO Qin－kun; GUO Peng
School of Electronic Information Engineering，Xi’an Technological University，Xi’an 710021，China
关键词:
手势特征提取; 手语识别; 多目标优化; 遗传算法
Keywords:
gesture feature extraction; sign language recognition; multi－objective optimization; genetic algorithm
分类号:
TP301.6
DOI:
10．3969/j．issn．1673－629X．2019．02．011
摘要:
手势是人类与计算机交互的直观方式，随着人工智能技术的发展，以机器为核心的计算模式正朝着以人为中心的计算模式转变，自然且符合人类习惯的人机交互(HCI)方式逐渐成为目前研究的热点。一个高效的人机交互系统应该以良好的识别精度和识别速度为目标。文中提出了一种基于深度信息的静态手势识别方法，包含了手势大小、光照和旋转变化等因素的影响。从识别精度和速度两个方面，综合比较了如 Hu 矩、Zernike 矩、伪 Zernike 矩、傅里叶描述符和 Gabor特征等几种常见的图像特征描述符。手势识别采用多层感知器，它具有结构灵活、识别速度快等特点。为了提高识别精度、减少计算量，特征向量和神经网络均通过基于 NSGA－II 的多目标进化算法调整。在对手语识别的进一步探究中，对所提出的方法的有效性进行验证
Abstract:
Gesture is an intuitive way for human beings to interact with computers． With the development of artificial intelligence technology，the machine－centric computing mode is shifting toward the human－centered computing mode，and the human－computer interaction (HCI) methods that are natural and in line with human habits are gradually become the focus of current research． An efficient humancomputer interaction system should aim for better recognition accuracy and recognition speed． For this，we present a static gesture recognition method based on depth information，including the influence of gesture size，lighting，and rotation changes． Several common image feature descriptors such as Hu moment，Zernike moment，pseudo Zernike moment，Fourier descriptor and Gabor feature are comprehensively compared from their respective recognition accuracy and speed． Gesture recognition uses a multi－layer sensor which has a flexible structure and fast recognition speed． In order to improve the recognition accuracy and reduce the computational complexity，the eigenvectors and the neural network are adjusted by the multi－objective evolutionary algorithm based on NSGA－II． In the further exploration of the recognition of the opponent language，the validity of the proposed method is verified．

相似文献/References:

[1]张岱 柯珂.一种基于SCHMM的手语识别方法[J].计算机技术与发展,2009,(07):149.
　ZHANG Dai,KE Ke.SCHMM for Sign Language Recognition[J].,2009,(02):149.
[2]孙丽娟 张立材 郭彩龙.基于视觉的手势识别技术[J].计算机技术与发展,2008,(10):214.
　SUN Li-juan,ZHANG Li-cai,GUO Cai-long.Technologies of Hand Gesture Recognition Based on Vision[J].,2008,(02):214.
[3]刘杨俊武,程春玲.基于关键帧和局部极值的手势特征提取算法[J].计算机技术与发展,2018,28(03):127.[doi:10.3969/ j. issn.1673-629X.2018.03.027]
　LIU Yang-junwu,CHENG Chun-ling.A Gesture Feature Extraction Algorithm Based on Key Frames and Local Extremum[J].,2018,28(02):127.[doi:10.3969/ j. issn.1673-629X.2018.03.027]

更新日期/Last Update: 2019-02-10