請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/66678完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 張瑞益(Ray-I Chang) | |
| dc.contributor.author | Chia-Ying Yang | en |
| dc.contributor.author | 楊佳穎 | zh_TW |
| dc.date.accessioned | 2021-06-17T00:50:42Z | - |
| dc.date.available | 2017-01-16 | |
| dc.date.copyright | 2012-01-16 | |
| dc.date.issued | 2011 | |
| dc.date.submitted | 2011-11-18 | |
| dc.identifier.citation | [1] 內政部統計處 警察機關受理刑事案件
http://sowf.moi.gov.tw/stat/week/week10030.doc [2] 內政部警政署 民生竊盜案概況 http://www.npa.gov.tw/NPAGip/wSite/ct?xItem=57911&ctNode=12594&mp=2 [3] R. J. Baron, “Mechanisms of human facial recognition,” International Journal of Man-Machine Studies, vol. 2, pp. 137-178, 1981. [4] S. L. Phung, A. Bouzerdoum, and D. Chai. “Skin Segmentation Using Color Pixel Classification: Analysis and Comparison”. IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 27, no. 1, pp. 148-154, Jan. 2005. [5] D. Chai and K. N. Ngan, “Face Segmentation Using Skin Color Map in Videophone Applications,” IEEE Transation Circuits and Systems for Video Technology, vol. 9, no. 4, pp. 551-564, June 1999. [6] K. Sobottka and I. Pitas, “A Novel Method for Automatic Face Segmentation, Facial Feature Extraction and Tracking,” Signal Processing: Image Comm., vol. 12, no. 3, pp. 263-281, 1998. [7] M. Soriano, B. Martinkauppi, S. Huovinen and M. Laaksonen, “Adaptive skin color modeling using the skin locus for selecting training pixels,” Pattern Recognition, vol. 36, no. 3, pp. 681-690, Mar. 2003. [8] M.J. Jones and J.M. Rehg, “Statistical Color Models with Application to Skin Detection,” Int’l J. Computer Vision, vol. 46, no. 1, pp. 81-96, Jan. 2002. [9] H. Wang and S.F. Chang, “A Highly Efficient System for Automatic Face Region Detection in MPEG Video,” IEEE Transation Circuits and Systems for Video Technology, vol. 7, no. 4, pp. 615-628, Aug. 1997. [10] R. O. Duda, P. E. Hart, and D. G. Stork, 'Pattern Classification.' John Wiley and Sons, 2001. [11] J. Yang and A. Waibel, “A Real-Time Face Tracker,” Proceedings IEEE Workshop Applications of Computer Vision, pp. 142-147, Dec. 1996. [12] B. Menser and M. Wien, “Segmentation and Tracking of Facial Regions in Color Image Sequences,” SPIE Visual Communications and Image Processing 2000, vol. 4067, pp. 731-740, June 2000. [13] H. Greenspan, J. Goldberger, and I. Eshet, “Mixture Model for Face Color Modeling and Segmentation,” Pattern Recognition Letters, vol. 22, pp. 1525-1536, Sep. 2001. [14] M. H. Yang and N. Ahuja, “Gaussian Mixture Model for Human Skin Color and Its Applications in Image and Video Databases,” SPIE Storage and Retrieval for Image and Video Databases, vol. 3656, pp. 45-466, Jan. 1999. [15] S.L. Phung, D. Chai, and A. Bouzerdoum, “A Universal and Robust Human Skin Color Model Using Neural Networks,” Proceedings IJCNN '01 International Joint Conference on Neural Networks, vol. 4, pp. 2844-2849, Jul. 2001. [16] S. Haykin, 'Neural Networks: A Comprehensive Foundation,' 2nd ed., Upper- saddle, N.J.: Prentice-Hall, 1999. [17] M. H. Yang, D. J. Kriegman, and N. Ahuja, ”Detecting faces in images: a survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 1, pp. 34-58, Jan. 2002. [18] G. Yang and T. S. Huang, “Human Face Detection in Complex Background,” Pattern Recognition, Vol. 27, No. 1, pp. 53-63, Jan. 1994. [19] Y. Guan, 'Robust Eye Detection from Facial Image based on Multi-cue Facial Information,' IEEE International Conference Control and Automation, pp. 1775-1778, 2007. [20] H. X. Jia and Y. J. Zhang, 'Fast Human Detection by Boosting Histograms of Oriented Gradients,' Fourth International Conference on Image and Graphics, ICIG 2007, pp. 683-688, 2007. [21] C. Geng and X. Jiang, 'Face recognition using sift features,' 16th IEEE International Conference on Image Processing, pp. 3313-3316, 2009. [22] K. Sobottka and I. Pitas, “Face localization and facial feature extraction based on shape and color information,” International Conference on Image Processing, vol. 3, pp. 483-486, Sep. 1996. [23] S. McKenna, S. Gong, and Y. Raja, “Modelling Facial Colour and Identity with Gaussian Mixtures,” Pattern Recognition, vol. 31, no. 12, pp. 1883-1892, 1998. [24] I. Craw, D. Tock, and A. Bennett, “Finding Face Features,” Proceedings Second European Conference Computer Vision, pp. 92-96, 1992. [25] A. Lanitis, C. J. Taylor, and T.F. Cootes, “An Automatic Face Identification System Using Flexible Appearance Models,” Image and Vision Computing, vol. 13, no. 5, pp. 393-401, 1995. [26] H. A. Rowley, S. Baluja, and T. Kanade, “Neural network-based face detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No.1, pp. 23-38, Jan. 1998. [27] A. Mohamed, Y. Weng, J. Jiang, and S. Ipson, 'Face detection based neural networks using robust skin color segmentation, IEEE 5th International Multi-Conference on Systems, Signals and Devices(SSD), pp. 1-5, JUL 2008. [28] E. Osuna, R. Freund, and F. Girosi, “Training support vector machine: An application to face detection,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 130-136, Jun. 1997. [29] C. Shavers, R. Li, and G. Lebby, 'An SVM-based approach to face detection,' Proceeding of the Thirty-Eighth Southeastern Symposium on System Theory, pp. 362-366, Mar. 2006. [30] L. Sirovich and M. Kirby, “Low-dimensional procedure for the characterization of human faces,” Journal of the Optical Society of America Optics, Image Science and Vision, Vol.4, No.3, pp. 519-524, Mar. 1987. [31] M. Turk and A. Pentland, “Eigenfaces for Recognition,” J. Cognitive Neuroscience, vol. 3, no. 1, pp. 71-86, 1991. [32] P. Viola and M. Jones, 'Robust real-time face detection,' International Journal of Computer Vision, vol. 57, pp. 137-154, 2004. [33] H. Zhu and S. Zhu, 'Face detection based on AdaBoost algorithm with differential images,' International Conference on Audio, Language and Image Processing (ICALIP), pp. 718-722, Jul. 2008. [34] OpenCV(Open Source Computer Vision) http://opencv.willowgarage.com/wiki/ [35] D. Chai and K. N. Ngan, “Face segmentation using skin-color map in videophone applications,” IEEE Transactions on Circuits System for Video Technology, vol. 9, pp. 551–564, June 1999. [36] 世界顏色人種比例 http://zh.wikipedia.org/wiki/%E6%A3%95%E8%89%B2%E4%BA%BA%E7%A7%8D http://zhidao.baidu.com/question/81580542 [37] P. Viola and M. J. Jones, 'Rapid Object Detection using a Boosted Cascade of Simple Features,' IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 511-518, 2001 [38] A. Jorgensen, 'AdaBoost and Histograms for Fast Face Detection,' Master’s Thesis in Computer Science (20 credits), Engineering Physics, Royal Institute of Technology, 2006 [39] 直方圖均衡化 Histogram equalization http://en.wikipedia.org/wiki/Histogram_equalization [40] Y. Y. Lin and T. L. Liu, 'Robust Face Detection with Multi-Class Boosting,' IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR), vol. 1, pp. 680-687, Jun. 2005. [41] R. Lienhart and J. Maydt, 'An Extended Set of Haar-like Features for Rapid Object Detection,' International Conference on Image Processing, vol. 1, pp. 900-903, 2002. [42] M. Sadeghi, A. Sadeghi, S. Nourizadeh, A. M. Ranjbar, and S. Azizi, 'Power System Security Assessment Using AdaBoost Algorithm,' In proceedings of the North American Power Symposium (NAPS'09), 2009 [43] W. Zhang, R. Tong, and J. Dong, 'Boosting 2-Thresholded Weak Classifiers over Scattered Rectangle Features for Object Detection,' Journal of Multimedia, vol. 4, no. 6, pp. 397-404, Dec. 2009. [44] R. Xiao, M. J. Li, and H. J. Zhang, 'Robust Multipose Face Detection in Images,' IEEE Transactions on Circuits and Systems for Video Technology, vol. 14, no. 1, pp. 31-41, Jan. 2004. [45] R. E. Schapire, 'The Strength of Weak Learnability,' 30th Annual Symposium on Foundations of Computer Science, vol. 5, no. 2, pp.197-227, 1990 [46] Y. Freund, 'Boosting a Weak Learning Algorithm by Majority,' Information and Computation, vol. 2, pp. 256-285, 1995. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/66678 | - |
| dc.description.abstract | 治安問題一直是政府及社會大眾常討論的課題之一,尤以層出不窮的竊盜案最為人所詬病。本篇論文提出了一套即時人臉偵測系統以輔助監控系統,僅需要一般的攝影器材就能達到偵測及追蹤人臉的功能。本系統由六個模組所組成,其中包含了膚色分割、候選臉部區域篩選、特徵訓練、視窗掃描、視窗偵測及臉部視窗膚色驗證。由於影像中大部分面積為非膚色,而人臉上佈滿膚色資訊,故本實驗充分利用膚色資訊作為前處理及人臉驗證,可大幅降低系統運算量。在偵測方面,使用AdaBoost演算法挑選出最佳的矩型特徵(Rectangle feature)組合,並利用積分影像(Integral image)可使掃描視窗快速計算出矩型特徵值。經實驗證明,本研究之偵測人臉方法能達到96.12%偵測率,且執行時間為OpenCV的12%~96%,在640x480的影像中,平均執行時間僅需80毫秒。 | zh_TW |
| dc.description.abstract | Law and order problem has been one of the topics that often discussed by the government and the society. Especially in the theft cases most criticized. This thesis proposes a set of real-time face detection system to supplement the monitoring system. The system includes six functions, Skin Color Segmentation, Filter Candidate Face Region, Training Features, Scanning Windows, Detection Window, Verify Face Window. As the image, non-skin color in most area, and the person’s face covered with skin color information. So, full use of skin color information as pre-process and verify faces can be greatly reduced system operator. In the detection, the use of AdaBoost algorithm to select the best combination of rectangle features. And the use of integral image, the scan windows to quickly calculate the value of rectangle feature. From experimental result, it was found 96.12 percent of correct face detection is achieved using the proposed method, and the execution time of 0.12~0.96 times the OpenCV. In the 640x480 image, the average execution time of only 80 ms. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T00:50:42Z (GMT). No. of bitstreams: 1 ntu-100-R98525090-1.pdf: 6737942 bytes, checksum: 3cad7dfcfafd593d29adaaaccdd9f4e3 (MD5) Previous issue date: 2011 | en |
| dc.description.tableofcontents | 摘要 i
ABSTRACT ii 論文目錄 iii 圖目錄 v 表目錄 vii 1 第一章、緒論 1 1.1 研究動機與目的 1 1.2 相關研究 2 1.3 論文架構 5 2 第二章、膚色區域偵測 7 2.1 色彩空間介紹 7 2.2 膚色模組建制 8 2.3 比較不同色彩空間膚色分割 11 2.4 候選臉部區域之擷取與篩選 14 2.5 候選臉部區域系統流程圖 19 3 第三章、利用AdaBoost人臉訓練 21 3.1 訓練樣本 22 3.2 特徵介紹與選擇 23 3.2.1 Haar-Like Features 23 3.2.2 積分影像Integral Image 26 摘要 i ABSTRACT ii 論文目錄 iii 圖目錄 v 表目錄 vii 1 第一章、緒論 1 1.1 研究動機與目的 1 1.2 相關研究 2 1.3 論文架構 5 2 第二章、膚色區域偵測 7 2.1 色彩空間介紹 7 2.2 膚色模組建制 8 2.3 比較不同色彩空間膚色分割 11 2.4 候選臉部區域之擷取與篩選 14 2.5 候選臉部區域系統流程圖 19 3 第三章、利用AdaBoost人臉訓練 21 3.1 訓練樣本 22 3.2 特徵介紹與選擇 23 3.2.1 Haar-Like Features 23 3.2.2 積分影像Integral Image 26 3.2.3 弱分類器 29 3.3 AdaBoost 訓練 32 3.4 級聯分類器 35 4 第四章、人臉偵測 37 4.1 掃描視窗 38 4.2 合併臉部視窗及膚色驗證 41 5 第五章、實驗結果與討論 47 5.1 訓練系統 47 5.2 系統流程 48 5.3 團體照偵測實作 50 5.4 即時系統實作 55 6 第六章、論文結果與討論 58 參考文獻 60 | |
| dc.language.iso | zh-TW | |
| dc.subject | 物件偵測 | zh_TW |
| dc.subject | 人臉偵測 | zh_TW |
| dc.subject | 膚色偵測 | zh_TW |
| dc.subject | AdaBoost | zh_TW |
| dc.subject | Face detection | en |
| dc.subject | Object detection | en |
| dc.subject | Skin segmentation | en |
| dc.subject | AdaBoost | en |
| dc.title | 以膚色資訊加速之AdaBoost即時人臉偵測系統 | zh_TW |
| dc.title | Using Skin-color information to AdaBoost Real-time Face Detection System | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 100-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 蔡進發,郭真祥,黃乾綱,張恆華 | |
| dc.subject.keyword | 人臉偵測,物件偵測,膚色偵測,AdaBoost, | zh_TW |
| dc.subject.keyword | Face detection,Object detection,Skin segmentation,AdaBoost, | en |
| dc.relation.page | 64 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2011-11-18 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 工程科學及海洋工程學研究所 | zh_TW |
| 顯示於系所單位: | 工程科學及海洋工程學系 | |
文件中的檔案:
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-100-1.pdf 未授權公開取用 | 6.58 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。
