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研究生:黃柏儒
研究生(外文):Bo-Ru Huang
論文名稱:用A*導引之霍夫轉換於智慧型家庭中個人身份辨識
論文名稱(外文):Person Identification by A*-Guided Generalized Hough Transform for Smart Home Applications
指導教授:張欽圳
指導教授(外文):Chin-Chun Chang
學位類別:碩士
校院名稱:國立臺灣海洋大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2007
畢業學年度:96
語文別:英文
論文頁數:37
中文關鍵詞:智慧型家庭A*導引式一般化霍福轉換
外文關鍵詞:smart home applicationA*-guided Generalized Hough Transform
相關次數:
  • 被引用被引用:0
  • 點閱點閱:180
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  • 下載下載:27
  • 收藏至我的研究室書目清單書目收藏:1
由於一般化霍福轉換(Generalized Hough Transform)可以在複雜背景的環境中辨識出待測物體以及找出其位置,因此蠻適合智慧型家庭中的應用,如以臉部輪廓登入某個系統,或以手勢來啟動某些特定的程式。但是一般化霍福轉換在多重樣版問題時,花了太多時間在沒有必要的投票上,因此本研究使用A*導引式一般化霍福轉換(A*-guided Generalized Hough Transform) 於智慧型家庭中的應用,來縮短一般化霍福轉換計算時間。實驗結果中證明了此演算法使用於個人身分辨識確實可行,並可以大幅縮短執行時間。
Since the generalized Hough transform (GHT) can be used to identify an object in a complicated environment, the GHT is suitable for smart home applications, such as the person-identification system based on face recognition and the interactive interface for the electronic appliance through hand gestures. However, the GHT for multiple templates may be slow because it may take too much computation time on unnecessary votes for templates which have no instances in the image. In this study, the A*-guided generalized Hough transform is used to reduce the computing time of the GHT for smart home applications. The experimental results have proved that the A*-guided GHT is faster than the GHT and has great potential for the smart home applications.
Chapter 1. Introduction 1
1.1. Motivation 1
1.2. Related Research 3
1.3. Overview of the proposed system 4
1.4. Thesis Organizations 7
Chapter 2. Review of the A*-Guided Generalized Hough Transform 8
2.1 A Review of the Generalized Hough Transform 8
2.2 A Review of the A* Search 9
2.3 A Review of the A*-guided GHT 10
Chapter 3. The Proposed System 13
3.1 Overview 13
3.2 The training phase 13
3.3. The recognizing phase 16
Chapter 4. Experimental Results 19
Chapter 5. Conclusion and Future Works 26
References 28
[1] Chin-Chun Chang, “A*-Guided Generalized Hough Transform for Multiple Shapes,” Submitted.
[2] Erik Hjelmas, and Boon Kee Low, “Face Detection: A Survey,” Computer Vision and Image Understanding, Vol. 83, pp. 236-274, 2001.
[3] William A. Barrett, “A Survey of Face Recognition Algorithms and Testing Results,” Conference record of the Thirty-first Asilomar Conference on Signal, Systems & Computers, Vol.1, pp. 301-305, 1997.
[4] Alex (Sandy) Pentland, and Tanzeem Choudhury, “Face Recognition for Smart Environments,” IEEE Computer Magazine, Vol. 33, Issue 2, pp. 50 - 55, 2000.
[5] Bertrand LEROY' Isabelle L. HEKIN' Laurent D. COHEN2, “Face Identification by Deformation Measure,” Proceedings of ICPR '96, pp. 633 - 637, 1996.
[6] Hongcheng Wang, and Narendra Ahuja, “Facial Expression Decomposition,” Proceedings of the Ninth IEEE International Conference on Computer Vision (ICCV’03), pp. 958 - 965, 2003.
[7] M.A. Kerin and T.J. Stonham, “Face Recognition Using a Digital Neural Network With Self-Organization Capabilities,” Proceedings of 10th International Conference on Pattern Recognition, Vol. 1, pp. 738-741, 1990.
[8] Linda G. Shapiro and George C. Stockman, Computer Vision, Prentice-Hall, 2001.
[9] D.B. Ballard, “Generalizing the Hough Transform to Detect Arbitrary Shapes,” Pattern recognition, Vol. 13, No. 2, pp 714 - 725, 1981.
[10] http://en.wikipedia.org/wiki/A*_search_algorithm.
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