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研究生:廖柏堯
研究生(外文):Bo-Yao Liao
論文名稱:多目標粒子群最佳化應用於膚色偵測
論文名稱(外文):Multi-Objective Particle Swarm Optimization For Skin Color Detection
指導教授:陸冠群陸冠群引用關係
指導教授(外文):Guan-Chun Luh
口試委員:陸冠群
口試委員(外文):Guan-Chun Luh
口試日期:2013-10-01
學位類別:碩士
校院名稱:大同大學
系所名稱:機械工程學系(所)
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2013
畢業學年度:102
語文別:中文
論文頁數:71
中文關鍵詞:色彩空間多目標粒子群最佳化膚色偵測閥值
外文關鍵詞:multi-objective particle swarm optimizationskin detectioncolor spacethreshold
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人臉辨識和人臉偵測普遍使用到的方法之一就是膚色偵測,本文提供一個以多目標粒子群最佳化結和色彩空間閥值的方法來做膚色偵測,採用不同色彩空間RGB、YCbCr、YCgCr三種色彩空間,並且排除代表亮度的Y,形成15種色彩元件組成的RGB-CbCrCg色彩空間,並制定出各個元件閥值上下限的範圍,加入膚色權重值,以多目標粒子群最佳化同時對膚色與非膚色做訓練,尋找最佳的色彩空間閥值範圍,並以ECU皮膚偵測資料庫的圖片做驗證。
One of the most common methods in face detection and face Recognition is skin detection, and this thesis is to detect skin through Multi-Objective Particle Swarm Optimization and thresholds in color space. This method uses three kinds of color spaces, which are RGB、YCbCr、YCgCr, excluding Y which represents brightness/illumination, and it becomes RGB-CbCrCg which is made of fifteen kinds of color spaces. By setting minimum and maximum of each threshold, and employing the combination of skin weighting, the method uses Multi-Objective Particle Swarm Optimization to train skin and non-skin, resulting in the best range of thresholds in color space, and it will be confirmed by the images derived from the ECU database.
目錄 IV
圖目錄 VIII
表目錄 X
第一章 緒論1
1.1 研究動機與目的1
1.2 文獻探討2
1.2.1 膚色偵測2
1.2.2 粒子群演算法4
1.3 論文架構5
第二章 粒子群演算法6
2.1 前言6
2.2 起源6
2.3 PSO的特點 7
2.4 PSO演算法基本結構8
2.5 單目標及多目標最佳化11
2.5.1 單目標最佳化(single-objective optimization)11
2.5.2 多目標粒子群最佳化12
第三章 皮膚偵測16
3.1 介紹16
3.2 皮膚色彩空間18
3.2.1 基本色彩空間(RGB,RGB,正規化CIE-XYZ)19
3.2.2 感知色彩空間(HSI,HSL,HSV,TSL)19
3.2.3 正交的色彩空間(YCbCr和YIQ,YUV,YES)20
3.2.4 感知均勻的色彩空間(CIE-Lab和CIE-Luv)20
3.2.5 其他的色彩空間21
3.3 皮膚偵測的方法21
3.3.1 閥值法22
3.3.2 直方圖模型與naiveBayes分類器23
3.3.3 斯分類法23
3.3.4 單高斯模型(SGM)23
3.3.5 高斯混合模型(GMM)24
3.3.6 色彩空間模型24
第四章 色彩空間26
4.1 簡介26
4.1.1 RGB色彩空間26
4.1.2 YCbCr色彩空間28
4.1.3 YCgCr色彩空間30
4.1.4 YDbDr色彩空間30
4.1.5 YPbPr色彩空間31
4.1.6 HSV色彩空間31
4.1.7 HSL色彩空間33
4.1.8 HSI色彩空間35
4.1.9 TSV與TSL色彩空間35
4.1.10 NCC色彩空間37
4.1.11 L*a*b*色彩空間37
4.1.12 CMY色彩空間39
4.1.13 XYZ色彩空間40
4.1.14 Yxy色彩空間40
4.1.15 YUV色彩空間41
4.1.16 YIQ色彩空間41
第五章 MOPSO應用於膚色偵測43
5.1 簡介43
5.2 原理43
5.3 膚色偵測步驟51
第六章 實驗結果與討論53
6.1 簡介53
6.2 ECU人臉偵測和皮膚偵測資料庫53
6.3 膚色偵測軟體54
6.4 實驗步驟57
6.5 實驗結果58
6.6 實驗討論64
第七章 結論與未來展望66
7.1 結論66
7.2 未來展望66


圖目錄
圖2.1粒子位置更新示意圖8
圖2.2粒子群最佳化演算法流程圖10
圖2.3 Pareto front示意圖14

圖4.1 RGB色光加色混色圖27
圖4.2 RGB色彩空間模型示意圖28
圖4.3 HSV的圖形描述32
圖4.4 HSV的圓錐模型33
圖4.5 HSL的圖形描述34
圖4.6 L*a*b*的圖形描述38
圖4.7 CMY色料減色混色圖39

圖5.1 RGB分佈圖44
圖5.2 (R-G)、(R-B)、(G-B)分佈圖45
圖5.3 G/R、B/R、B/G分佈圖46
圖5.4 CbCrCg分佈圖47
圖5.5 、 、 分佈圖 48
圖5.6膚色與非膚色偵測示意圖50

圖6.1原始圖片54
圖6.2膚色分割區塊54
圖6.3視窗介面-目錄55
圖6.4視窗介面-參數55
圖6.5視窗介面-執行56
圖6.6訓練樣本ROC曲線圖58
圖6.7測試樣本ROC曲線圖59
圖6.8偵測結果比較(1)原始圖(2)Propose(3)K. Sobottkaet al.[29](4)Wang and Yuan[4](5)J.L Crowley and J. Coutaz[40]61
圖6.9 (6)Peer et al.[5] (7)J. Orozco and J. Villanueva[41] (8)Chiunhsiun Lin[42] (9)Chai and Ngan[3] (10)G. Kukharev and A. Nowosielski[7]62
圖6.10 (11)Phung and Chai[43] (12)Chang and Tseng[44] (13) A. Berbar and A. Kandeel[45] (14) P. S. Hiremathand AJIT Danti[46] (15) V.Girondel andL.Bonnaud[47]63
圖6.11 (16) J.J. de Dios and N. Garcia[25] (17) Maand Xiao[48] (18) Zhang and Shi[49] (19)J.J. de Dios, N. Garcia[50]64


表目錄
表1.1 色彩空間轉換4

表5.149
表5.2閥值最大值和最小值的範圍51

表6.1 ECU資料庫資料集54
表6.2皮膚資料集統計資料54
表6.3膚色偵測(TP、FP)比較60
[1]P. Kakumanu , S. Makrogiannis, and N. Bourbakis, “A survey of skin-color modeling and detection methods”, Pattern Recognition, Vol. 40, pp. 1106-1122, (2007).
[2]V. Vezhnevets, V. Sazonov, and Alla Andreev, “A survey on pixel-based skin color detection techniques”, International Conference Graphicon, pp. 85-92,(2003).
[3]D. Chai, K.N. Ngan,” Face segmentation using skin colour map in videophone applications”, IEEE Transactions on Circuits and Systems for Video Technology 9 (4) (1999) 551-564.
[4]Y. Wang, B. Yuan, “A novel approach for human face detection from color images under complex background”, Pattern Recognition 34 (10) (2001) 1983-1992.
[5]J. Kovac, P. Peer, F. Solina, “2D Versus 3D Colour Space Face Detection”, in: 4th EURASIP Conference on Video/Image Processing and Multimedia Communications, Croatia, (2003), pp. 449-454.
[6]Giovani Gomez and Eduardo F. Morales. “Automatic feature construction and a simple rule induction algorithm for skin detection”. In ICML, pages 31-38, (2002).
[7]G. Kukharev, A. Nowosielski, “Visitor Identification-Elaborating Real Time Face Recognition System”pp 157-164, Feb (2004).
[8]B. C. Ennehar, O. Brahim, and T. Hicham, “An Appropriate Color Space to Improve Human Skin Detection“, Journal of Computer Science.
[9]J. Kennedy, R. Eberhart, “Particle swarm optimization”, IEEE, November (1995).
[10]K. Veeramachaneni, T. Peram, C. Mohan, L. A. Osadciw, “Optimization using particle swarms with near neighbor interactions”, Proceedings of the GECCO, Vol. 2723, pp. 110-121, (2003).
[11]B. Liu, L. Wang, Y. H. Jin, F. Tang, D. X. Huang, “Improved particleswarm optimization combined with chaos”, Chaos, Solitons andFractals, Vol. 25, No. 5,September (2005).
[12]Y. Dong, J. Tang, B. Xu, D. Wang, “An application of swarm optimization to nonlinear programming”, Computers andMathematics with Applications, No. 49, February (2005).
[13]A. Leontitsis, D. Kontogiorgos, J. Pagge, “Repel the swarm to the optimum!”, Applied Mathematics and Computation, Vol. 173, No. 1,February (2006).
[14]J. Moore and R. Chapman. Application of particle swarm to multi objective optimization, Department of Computer Science and Software Engineering, Auburn University,(1999).
[15]Pan Q-K, Tasgetiren MF, Liang Y-C. A discrete particle swarm optimization algorithm for the no-wait flowshop scheduling problem. ComputOper Res (2008).
[16]Esmin, A.A.A., A.R. Aoki, G. Lambert-Torres, “Particle Swarm Optimization for Fuzzy Membership Functions Optimization,” IEEE International Conference, Vol. 3, (2002).
[17]J Salerno, “Using the particle swarm optimization technique to train a recurrent neural model,” In Proceedings of the Ninth IEEE International Conference on Tools with Artificial Intelligence, pp. 45-49, (1997).
[18]T. Sousa, A. Silva, and A. Neves, “Particle swarm based data mining algorithms for classification tasks,” ELSEVIER on Parallel Computing, NO.30, pp.767-783.
[19]Ayed Salman, Imtiaz Ahmad, and Sabah Al-Madani, “Particle swarm optimization for task assignment problem,” Microprocessors and Microsystems 26, pp. 363-371, (2002).
[20] Shi Y, Eberhart RC. A modified particle swarm optimizer . In: Proceedings of the IEEE international conference on evolutionary computation; (1998).
[21]Angeline PJ. Evolutionary optimization versus particle swarm optimization: philosophy and performance difference. In: Proceeding of the evolutionary programming conference; (1998).
[22]Eberhart RC, Shi Y. Particle swarm optimization: developments, Applications and resources. In: Proceedings of the IEEE congress on evolutionary computation; (2001), pp. 81-86.
[23]許志義,”多目標決策”。 五南圖書出版社(2003)
[24]Tomaschitz, J.A., Facon, J., “Skin detection applied to multi-racial Images”, IWSSIP 16th International Conference on Systems, Signalsand Image Processing, pp. 1 - 3, (2009).
[25]VladimirVezhnevets, VassiliSazonov, AllaAndreeva, “A Survey On Pixel-Based Skin Color Detection Techniques,” International Conference Graphicon,pp. 85-92, (2003).
[26]J.J. de Dios, N. Garcia, "Face detection based on a new color space YCgCr." Image Processing, (2003). ICIP (2003). Proceedings. (2003) International Conference on. Vol. 3. IEEE, (2003).
[27]G. Gomez, M. Sanchez, L.E. Sucar, “On selecting an appropriate colour space for skin detection”, Springer-Verlag: Lecture Notes in Artificial Intelligence, vol. 2313, (2002), pp. 70-79.
[28]J. Brand, J. Mason, “A comparative assessment of three approaches to pixel level human skin-detection”, ICPR01 1 (2000), pp. 1056-1059.
[29]Jose M. Chaves-Gonzalez, M. A. Vega-Rodriguez, J. A. Gomez-Pulido, and J. M. Sanchez-Perez, “Detecting skin in face recognition systems: A colour spaces study”, Digital Signal Processing,Vol. 20, pp. 806-823, (2010).
[30]K. Sobottka, I. Pitas, “A novel method for automatic face segmentation, facial feature extraction and tracking, Signal Process”. Image Commun. 12 (1998), pp. 263-281.
[31]K. Sobottka, I. Pitas, “Extraction of facial regions and features using
color and shape information”, ICPR96, (1996).
[32]Y. Dai, Y. Nakano, “Face-texture model based on SGLD and its application in face detection in a color scene”, Pattern Recognition 29 (6) (1996), pp. 1007-1017.
[33]M.J. Jones, J.M. Rehg, “Statistical color models with application to skin detection”, CVPR99, (1999)
[34] M.H. Yang, N. Ahuja, “Gaussian Mixture model for human skin color and its application in image and video databases”, Proceedings of SPIE: Conference on Storage and Retrieval for Image and Video Databases, vol. 3656, (1999), pp. 458–466.
[35]S. Phung, A. Bouzerdoum, D. Chai, “A novel skin color model inYCbCr color space and its application to human face detection”, 2002 International Conference on Image Processing, Vol 1, pp. I-289 -I-292, (2002).
[36]Y. Zou, W. Chen and J. Zhang, “Edge map guided stereomatching in HSL color space for mobile robot navigation”, ROBIO, pp. 841-846. IEEE, (2011).
[37]Albayrak, Songul, “Color Quantization by Modified K-Means Algorithm”, Journal of Applied Science, Vol 1, Issue 4, pp. 508-511, (2001).
[38]B.C. Ennehar, O. Brahim, and T. Hichan, “An Appropriate Color Space to Improve Human Skin Detection”, Journal of Computer Science, Vol 9, pp.18-27, (2011).
[39]Nabiyev, Vasif, and Asuman Gunay. "Towards a biometric purpose image filter according to skin detection." The Second International Conference “Problems of Cybernetics and Informatics” September. (2008), pp. 10-12.
[40]J.L. Crowley, “Vision for man-machine interaction”. Robotics and Autonomous Systems, 19(3), pp.347-358, (1997).
[41]Al Haj, Murad, et al. "Automatic face and facial features initialization for robust and accurate tracking." Pattern Recognition, (2008). ICPR (2008). 19th International Conference on. IEEE, (2008).
[42]Chiunhsiun Lin, "Face detection by color and multilayer feedforward neural network." Information Acquisition, (2005) IEEE International Conference on. IEEE, (2005).
[43]R.C. Chang, F.C. Tseng, "Automatic detection and correction for glossy reflections in digital photograph." Ubi-media Computing (U-Media), 2010 3rd IEEE International Conference on. IEEE, 2010.
[44] Mohamed A. Berbar, Hamdy M. Kelash, and Amany A. Kandeel. "Faces and facial features detection in color images." Geometric Modeling and Imaging--New Trends, (2006). IEEE, 1993.
[45]P.S. Hiremath and AJIT Danti, "Detection of multiple faces in an image using skin color information and lines-of-separability face model." International Journal of Pattern Recognition and Artificial Intelligence 20.01 (2006): 39-61.
[46]V. Girondel, L. Bonnaud, Alice Caplier, "Hands detection and tracking for interactive multimedia applications." Proceedings of the International Conference on Computer Vision and Graphics-ICCVG. (2002).
[47]J. Ma, R. Xiao, K.H.B. Ghazali, "Driver's Face Tracking Based On Improved CAMShift for Drowsiness Detection." situations 4 (2012): 5.
[48]Zhengzhen, Zhang, and Shi Yuexiang. "Skin color detecting unite YCgCb color space with YCgCr color space." Image Analysis and Signal Processing, (2009). IASP (2009). International Conference on. IEEE, (2009).
[49]J.J. de Dios, N. Garcia, "Feature extraction used for face localization based on skin color." Image Analysis and Recognition. Springer Berlin Heidelberg, (2005). 1032-1039.
[50]C.H. Yang, X.S. Qian, and W.H. Gui. "Hybrid algorithm of chaotic differential evolution and particle swarm optimization." Jisuanji Yingyong Yanjiu 28.2 (2011): 439-441.
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