淡江大學機構典藏:Item 987654321/98490
English  |  正體中文  |  简体中文  |  全文笔数/总笔数 : 62822/95882 (66%)
造访人次 : 4015673      在线人数 : 533
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜寻范围 查询小技巧：
  • 您可在西文检索词汇前后加上"双引号"，以获取较精准的检索结果
  • 若欲以作者姓名搜寻，建议至进阶搜寻限定作者字段，可获得较完整数据
    •  进阶搜寻


    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/98490


    题名: A Real-time Sign Language Recognition System for Hearing and Speaking Challengers
    作者: Hsieh, Chieh-Fu;Chen, Li-Ming;Huang, Ku-Chen;Hsieh, Ching-Tang;Yih, Chi-Hsiao
    贡献者: 淡江大學電機工程學系;淡江大學體育事務處體
    关键词: Sign language;Kinect;Human Machine Interface (HMI);hidden Markov model (HMM)
    日期: 2014-07-12
    上传时间: 2014-08-07 18:08:09 (UTC+8)
    出版者: Taipei: Asia-Pacific Education & Research Association
    摘要: Sign language is the primary means of communication between deaf people and hearing/speaking challengers. There are many varieties of sign language in different challenger community, just like an ethnic community within society. Unfortunately, few people have knowledge of sign language in our daily life. In general, interpreters can help us to communicate with these challengers, but they only can be found in Government Agencies, Hospital, and etc. Moreover, it is expensive to employ interpreter on personal behalf and inconvenient when privacy is required. It is very important to develop a robust Human Machine Interface (HMI) system that can support challengers to enter our society. A novel sign language recognition system is proposed. This system is composed of three parts. First, initial coordinate locations of hands are obtained by using joint skeleton information of Kinect. Next, we extract features from joints of hands that have depth information and translate handshapes. Then we train Hidden Markov Model-based Threshold Model by three feature sets. Finally, we use Hidden Markov Model-based Threshold Model to segment and recognize sign language. Experimental results show, average recognition rate for signer-dependent and signer-independent are 95% and 92%, respectively. We also find that feature sets including handshape can achieve better recognition result.
    關聯: Proceedings of International Research Conference on Information Technology and Computer Sciences, pp.24-31
    显示于类别:[體育事務處] 會議論文
    [電機工程學系暨研究所] 會議論文

    文件中的档案:

    档案 描述 大小格式浏览次数
    IRCITCS-266_A Real-time Sign Language Recognition System for Hearing and Speaking Challengers.pdf會議論文內容613KbAdobe PDF903检视/开启

    在機構典藏中所有的数据项都受到原著作权保护.

    TAIR相关文章

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - 回馈