Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/4885
標題: Hand Gesture Segmentation Using 3D Stereo Vision
3D立體視覺於手勢切割之應用
作者: Cheng, Chiao-Yun
鄭喬勻
關鍵字: 立體視覺
stereo vision
深度資訊
標籤演算法
Parzen Window
膚色偵測
形態學演算法
手腕偵測
最小平方法線
depth information
labeling algorithm
skin detection
morphological algorithm
wrist detection
出版社: 通訊工程研究所
引用: 參考文獻 [1] D. Comaniciu, P. Meer, Mean shift: a robust approach toward feature space analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence 24 (2002), pp.603–619. [2] A.Y. Yang, J. Wright, Y. Ma, S.S. Sastry, Unsupervised segmentation of natural images via lossy data compression, Computer Vision and Image Understanding 110 (2008), pp. 212–225. [3] L. Bertelli, B. Sumengen, B.S. Manjunath, F. Gibou, A variational framework for multiregion pairwise-similarity-based image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence 30 (2008), pp.1400–1414. [4] P. Arbelaez, M. Maire, C. Fowlkes, J. Malik, From contours to regions: an empirical evaluation, in: International Conference on Computer Vision and Pattern Recognition (CVPR), 2009, pp. 2294–2301. [5] L.Busin, N. Vandehbroucke, L. Macaire, and J-G. Postaire, “Color Space Selection for Unsupervised Color Image Segmentation by Histogram Multi-Thresholding,” IEEE Conference on, Image Processing, ICIP’04., vol. 1, pp. 203-206, Oct. 2004. [6] Nuno Vieira Lopes,Pedro A.Mogadouro do Couto,Humberto Bustince ,Pedro Melo-Pinto,“Automatic Histogram Threshold Using Fuzzy Measures” IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 19, NO. 1, JANUARY 2010 [7] R. C. Gonzalez, and R. E. Woods, “Digital image processing,” Second Edition, Prentice Hall 2002. [8] J. F. Canny, “A computational approach to edge detection,” IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 8, No. 6, 1986, pp. 679–698. [9] L. Ding, and A. Goshtasby, “On the Canny edge detector,” Pattern Recognition, Vol. 34, 2001, pp.721-725. [10] J. Basak, B. Chanda, and D. D. Majumder, “On Edge and Line Linking with Connectionist Models,” IEEE Transaction on System, Man, and Cybernetics, vol. 24, pp. 413-428, Mar. 1994. [11] D. Wang, “A multiscale gradient algorithm for image segmentation using watersheds,” Pattern Recognition, Vol. 30, Issue: 12, 1997, pp. 2043-2052. [12] N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Transactions of Systems, Man, and Cybernetics 9, 1979, pp.62–66. [13] S. A. Hojjatoleslami and J. Kittler, “Region Growing: A New Approach,” IEEE Trans. Image Processing, vol. 7, pp. 1079-1084, Jul. 1998. [14] S. Chen, W. Lin, and C. Chen, “Split-and-Merge Image Segmentation Based on Localized Feature Analysis and Statistical Tests,” CVGIP: Graph. Models Image Processing, vol. 53, pp. 457-475, Sep. 1991. [15] T. Pavlidis and Y. Liow, “Integrating Region Growing and Edge Detection,” IEEE Trans. Pattern Anal. Machine Intell., vol. 12, pp. 225-233, Mar. 1990. [16] L. D. Griffin, A. C. F. Colchester, and G. P. Robinson, “Scale and Segmentation of Gray-Level Images Using Maximum Gradient Paths,” Image Vis. Comput., vol. 10, pp.389-402, 1992. [17] Anjin Park, Sungju Yun, Jungwhan Kim, Seungk Min, and Keechul Jung Real-Time Vision-based Korean Finger Spelling Recognition System”,Proceedings of world academy of science,engineering and technology volume 34 october 2008 ISSN 2070-3740. [18] T. Starner, J. Weaver and A. Pentland, “Real-timeAmerican Sign Language Recognition Using Desk and Wearable Computer Based Video,” in Transaction of Pattern Analysis and Machine Intelligence, vol. 20(2), pp.1371-1375. [19] V. Mezaris,1,2 I. Kompatsiaris2 and M. G. Strintzis1,2,“Still Image Objective Segmentation Evaluation using Ground Truth”, 1 Information Processing Laboratory, Electrical and computer Engineering Department,Aristotle University of Thessaloniki, Thessaloniki 54124, Greece 2 Informatics and Telematics Institute, 1st Km Thermi- Panorama Rd, Thessaloniki 57001, Greece, 5th COST 276 Workshop (2003), pp. 9–14. [20] Mahmoud Elmezain, Ayoub Al-Hamadi, J‥org Appenrodt, and Bernd Michaelis, “ A Hidden Markov Model-Based Isolated and Meaningful Hand Gesture Recognition”, International Journal of Computer Systems Science and Engineering 5:2 2009 [21] Reza Hassanpour1,2 Stephan Wong1 Asadollah Shahbahrami1,3 ,“VisionBased Hand Gesture Recognition for Human Computer Interaction: A Review”, IADIS International Conference Interfaces and Human Computer Interaction 2008. [22] Radha, V.,Krishnaveni, M. ”Threshold based Segmentation using median filter for Sign language recognition system” Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on, pp.1394 – 1399 [23] http://zh.wikipedia.org/wiki/File:HSV_cylinder.png [24] http://zh.wikipedia.org/wiki/File:Color_cones.png [25] http://140.115.11.235/~chen/course/vision/ch5/ch5.htm [26] B. F. Wu, S. P. Lin, and C. C. Chiu, “Extracting characters from real vehicle licence plates out-of-doors,” Computer Vision, IET , vol.1, no.1, pp. 2-10, Mar. 2007. [27] 李經寧,「即時手勢辨識系統應用於機上盒控制」,國立中央大學資訊工程研究所碩士論文,民國九十八年十二月 [28] 張宸銘,「應用視訊之自動化手勢軌跡追蹤系統」,中原大學資訊工程研究所碩士論文,民國九十八年七月 [29] http://www.apostar.com.tw/ptgrey.html [30] [30] P.J.M Aarts and Van Laarhoven;”Simulated annealing and applications: Mathematics and its applications,” D. Reidel Publishing Company, 1987. [31] Lew, M.S., Huang, T.S., Wong, K.W., “Learning and feature selection in stereo matching,” IEEE Transactions on Pattern Analysis and Machine Intelligence , Vol.16, No.9, pp.869-881, 1994. [32] Ninad Thakoor, Student Member, IEEE, Jean Gao, Member, IEEE, and Venkat Devarajan, Senior Member, IEEE, “Multistage Branch-and-Bound Merging for Planar Surface Segmentation in Disparity Space”, IEEE Transactions on Image Processing , Volume :17 , Issue:11 [33] 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. [34] THEODORIDIS , PATTERN RECOGNITION 4/E ,全華圖書股份有限公司,December 4,2008 [35] T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein, Introduction to Algorithms, Second Edition, MIT Press, Cambridge, MA, 2001.
摘要: 本篇論文主要是研究手勢切割。我們可將這個研究分為手部區域的切割與手腕偵測兩個部份。首先是完整手勢影像的取得,透過雙鏡頭立體視覺攝影機獲得彩色影像、灰階影像與深度資訊後,將影像與深度資訊利用標籤演算法、Parzen Window的膚色偵測演算法以及形態學演算法進行處理。經過這些處理我們感興趣的完整手勢便可以從複雜背景中分割出來。再來就是如何準確且有效率地找到手腕的部份,在手腕偵測的部份,我們計算手部最大內接圓來決定手臂的上、下界以避免不同的手勢對於最小平方線在運算上造成的影響,並且求得手臂的最小平方法線與邊緣輪廓。利用找出手勢影像邊緣輪廓合適直線的方式,計算出手臂輪廓與合適直線的誤差,找到最大誤差點即為手腕的位置。根據垂直最小平方線的斜率與最大誤差點來精確的推算出手腕分割線並且切割出完整的手勢。最後經過與其他切割方式比較之後,可以發現我們提出的手勢分割方法所求得的手腕分割割線更加接近使用者實際的手腕位置,並且在主觀視覺上擁有較好的切割效果。
In this thesis, we study the hand gesture segmentation with a complex background. We can roughly divide the research into two parts: the hand/arm segmentation and the wrist detection. The first one is to obtain the complete hand/arm image region. We can acquire color images, gray-level images, and depth information through the stereo camera. Then we make use of the labeling, skin detection, and morphological algorithms to segment the hand/arm region from the complex background. The second part is to accurately locate the wrist. We calculate the maximum inner circle of the hand/arm region to decide the upper and lower thresholds for the wrist and to avoid the adverse influence due to different hand gestures. Then we obtain the edge contour of the arm. The contour is used to fit piecewise line segments. The point with the maximum fitting deviation is considered as the wrist position. Finally, the hand region can be segmented by removing the region below the wrist line. According to the experimental results, our approach can obtain visually better segmentation and the segmentation line which is nearer the position of real human wrist compared with other segmentation methods.
URI: http://hdl.handle.net/11455/4885
其他識別: U0005-0307201213052200
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-0307201213052200
Appears in Collections:通訊工程研究所

文件中的檔案:

取得全文請前往華藝線上圖書館



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.