English  |  正體中文  |  简体中文  |  Items with full text/Total items : 49287/83828 (59%)
Visitors : 7152112      Online Users : 35
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/53632


    Title: A Semiautomatic Content Adaptation Authoring Tool for Mobile Learning
    Authors: Chang, Hsuan-pu;Wang, Chun-chia;Shih, Timothy K.;Chao, Louis R.;Yeh, Shu-wei;Lee, Chen-yu
    Contributors: 淡江大學資訊與圖書館學系;淡江大學資訊工程學系
    Keywords: Face recognition;principle component analysis;PCA;two dimensional principle component analysis;2DPCA;discrete cosine transformation;DCT;weighted voting;spatial domain;frequency domain;genetic algorithms
    Date: 2008
    Issue Date: 2013-03-12 11:03:47 (UTC+8)
    Publisher: Heidelberg: Springer
    Abstract: Face Recognition is an important topic in the field of pattern recognition. This technology has a variety of applications including entrance guard control, personal service system, criminal verification, and security verification of finance. Our research focuses on the development of a human face recognition system. It is a challenge to correctly identify a human in an image under various possible situations including difference of lighting conditions, change of hairstyles, variation of facial expression, and different aspects of the face. We have analyzed several existing face recognition techniques and found that each of them is performed well over some specific sets of testing samples but poorly over some other sets. This motivates us to combine different techniques to construct a better face recognition system. First, we propose a new module E-2DPCA applying DCT for image enhancement and 2DPCA for feature extraction. The experimental results show that the recognition accuracy of E-2DPCA is better than all the modules we have analyzed. We choose the best two from those analyzed and compared them with our proposed E-2DPCA module, and found that although the E-2DPCA module outperforms the other two modules, each of the three modules behaves better than others over some specific set of samples. Thus we combine the three modules and apply weighted voting scheme to choose the recognition result from those given by the three modules. Experimental results show that the integrated system can further improve the recognition rate.
    Relation: Lecture Notes in Computer Science 5145, pp.529-540
    DOI: 10.1007/978-3-642-02568-6_9
    Appears in Collections:[俄羅斯研究所] 學位論文
    [資訊與圖書館學系暨研究所] 期刊論文

    Files in This Item:

    File SizeFormat
    index.html0KbHTML219View/Open

    All items in 機構典藏 are protected by copyright, with all rights reserved.


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