成功大學博碩士論文系統

簡易檢索 / 詳目顯示

回結果列表

研究生：	胡冠宇 Hu, Guan-Yu
論文名稱：	基於膚色之裸體影像偵測之研究 The Study on Naked People Image Detection Based on Skin Color
指導教授：	王明習 Wang, Ming-Shi
學位類別：	碩士 Master
系所名稱：	工學院 - 工程科學系 Department of Engineering Science
論文出版年：	2004
畢業學年度：	92
語文別：	中文
論文頁數：	71
中文關鍵詞：	裸體影像、膚色偵測
外文關鍵詞：	skin color, naked people image
相關次數：	點閱：55 下載：7
分享至:	分享至facebook 分享至twitter

查詢本校圖書館目錄查詢臺灣博碩士論文知識加值系統勘誤回報

　　本論文在以膚色為基礎的裸體影像偵測研究上，提出一個新方法，以將裸體影像分成三大類特徵模組的方式來加以偵測。首先，採用Bayesian YCbCr膚色偵測模型，來做為膚色偵測的方法。再將含大量膚色的影像丟入裸體影像分類流程中，以分類出裸體影像和非裸體影像。而這些分類流程中，包含臉部特寫影像分類器，而用來過濾會影響裸體偵測效能的臉部特寫影像；全裸影像分類器，用來過濾膚色大量集中在軀體上，影像中人物一絲不掛之全裸圖；胸前特徵影像分類器，利用顏色和亮度上的差異，以及胸前特徵與膚色間之關係，來切割出胸前特徵，並以之作為裸露胸前特徵影像判別之依據；裸露私處部位影像分類器，利用快速物件分析的方式，分析膚色像素在影像上的分布，來切割出私處部位，並以之作為判斷之依據。本論文，測試了從網路下載總共1538張影像，研究成果有89.79%正確率，在未來不但有助於色情影像上的防堵，也能提供作為影像內容擷取技術上的參考。

　　This thesis presents a new method for judging whether there are any naked people in an image based on skin color. The system consists of five stages. At first, The Bayesian YCbCr Skin Color Model is used to detect naked skin areas of the image and segmented it out roughly. Second. Haar face detector is used to avoid a facial closed-up shot. And then, a full-naked body image classifier is used to filter out the images which contain a naked people with no clothes on his body, and the nipple classifier is used to filter out these images which appear nipples on the human body. Finally, the private part classifier is used to filter out the images which contain people with her privates naked. Our proposal algorithm perform a recognition ratio of 89.79% with testing total 1538 images which were downloaded from the Internet and it is helpful both for the blue-picture-website-filter and for the semantic image indexing in the Content-based Image Retrieval.

章節目錄

中文摘要.........................................................i
Abstract........................................................ii
誌謝...........................................................iii
目錄............................................................iv
圖目錄..........................................................vi

第一章	緒論..................................................1
1	研究動機與目的...........................................1
2	過去之相關研究...........................................3
3	研究方法概述.............................................5
4	本文大綱.................................................6
第二章	膚色偵測技術..........................................7
1	色彩空間的選擇...........................................8
1.1	在膚色偵測技術上最好的色彩空間...........................8
1.2	YCBCR色彩空間...........................................10
2	膚色分類模型............................................12
2.1	BAYESIAN YCBCR膚色模型..................................12
2.2	BAYESIAN YCBCR膚色模型訓練流程..........................14
第三章	裸體影像分類器.......................................20
1	臉部特寫影像分類器......................................22
1.1	HAAR臉部偵測技術........................................24
1.2	臉部特寫影像分類........................................25
2	全裸影像分類器..........................................26
3	胸前特徵影像分類器......................................28
3.1	胸前特徵圖轉換(NIPPLE MAP TRANSFORMATION)...............29
3.2	胸前特徵圖的影像之加強(NIPPLE MAP ENHANCEMENT)..........30
3.3	胸前特徵的切割(NIPPLE MAP SEGMENTATION).................33
3.3.1	類似橢圓的形狀..........................................34
3.3.2	SOBEL邊界...............................................35
3.3.3	大量膚色環境............................................36
4	私處部位分類器..........................................38
4.1	應用SOBEL邊緣偵測的前處理...............................39
4.2	應用快速分析物件的前處理................................40
4.3	私處部位特徵的擷取......................................46
第四章	實驗結果與討論.......................................49
1	BAYESIAN YCBCR膚色模型測試結果..........................50
2	HAAR臉部特寫影像分類器測試結果..........................53
3	全裸影像分類器測試結果..................................55
4	胸前特徵影像分類器測試結果..............................58
5	私處部位影像分類器測試結果..............................61
6	整個裸體偵測系統測試結果................................64
第五章	結論與未來展望.......................................66
1	結論....................................................66
2	未來展望................................................67


                                    

[1] A. Albiol, L. Torres, and E.J. Delp, “Optimum Color Spaces for Skin Detection,” Image Processing, 2001. Proceedings. 2001 International Conference on, IEEE, Thessaloniki Greece, Vol. 1, pp. 122-124, Oct. 2001.
[2] Bimbo, A.D., Visual Information Retrieval, Morgan Kaufmann Publishers, Inc., San Francisco, California, 1999.
[3] CCIR, “Encoding parameters of digital television for studios”, CCIR Recommendation 601-2, Int. Radio Consult. Committee, Geneva, Switzerland, 1990.
[4] D. Chai and A. Bouzerdoum, “A Bayesian Approach to Skin Color Classification in YCbCr Color Space,” TENCON 2000. Proceedings, IEEE, Kuala Lumpur Malaysia, Vol. 2, pp. 421-424, Sept. 2000.
[5] L.L. Cao, X.L. Li, N.H. Yu, and Z.K. Liu, “Naked People Retrieval Based on Adaboost Learning,” Machine Learning and Cybernetics, 2002. Proceedings. 2002 International Conference on, IEEE, Vol. 2, pp. 1133-1138, Nov. 2002.
[6] D.A. Forsyth, and M.M. Fleck, “Identifying Nude Pictures,” Applications of Computer Vision, 1996. WACV '96., Proceedings 3rd IEEE Workshop on, IEEE, Sarasota, FL USA, pp. 103-108, Dec. 1996.
[7] Y. Freund and R.E. Schapire, “Experiments with a New Boosting Algorithm,” in Proceedings of theThirteenth International Conference. on Machine Learning, pp. 148-156, 1996.
[8] Y. Freund and R.E Schapire, “A decision-theoretic generalization of on-line learning and an application to boosting,” Journal of Computer and System Sciences, Vol. 55, pp. 119-139, 1997
[9] R.L. Hsu, A.M. Mohamed, and A.K. Jain, “Face Detection in Color Images,” IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol. 24, No. 5, pp. 696-706, May 2002.
[10] F. Jiao, W. Gao, L. Duan, and G. Cui, “Detecting Adult Image Using Multiple Features,” Info-tech and Info-net, 2001. Proceedings. ICII 2001 - Beijing. 2001 International Conferences on, IEEE, Beijing China, Vol. 3, pp. 378-383, Nov. 2001.
[11] R. Lienhart, A Kuranov, and V. Pisarevsky, “Empirical Analysis of Detection Cascades of Boosted Classifiers for Rapid Object Detection,” Technical report, MRL, Intel Labs, 2002.
[12] A.J. Smola and B. Schölkopf, “A Tutorial on Support Vector Regression,” NeuroCOLT Technical Report NC-TR-98-030, Royal Holloway College, University of London, UK, 1998.
[13] A.W.M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain, “Content-Based Image Retrieval at The End of Early Years,” IEEE Trans. On PAMI, Vol. 22, no.12, pp. 1349-1380, Dec. 2000.
[14] P. Vioal and M. Jones, “Robust Real-time Object Detection,” In Proc. of IEEE Workshop on Statistical and Theories of Computer Vision, 2001.
[15] V. Vezhnevets, V. Sazonov, and A. Andreeva, “A Survey on Pixel-Based Skin Color Detection Techniques,” Proc. Graphicon-2003, pp. 85-92, Moscow, Russia, Sep. 2003.
[16] M.H. Yang, D.J. Kriegman, and N. Ahuja, “Detecting faces in images: a survey,” Pattern Analysis and Machine Intelligence, IEEE Transactions on , Vol. 24, pp. 34 - 58, Jan. 2002.
[17] Open Source Computer Vision Library (OpenCV) http://sourceforge.net/projects/opencvlibrary/

校內：2005-08-04公開
校外：2005-08-04公開

簡易檢索 / 詳目顯示

相關論文