政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/32697

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/32697

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政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 > Item 140.119/32697

請使用永久網址來引用或連結此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/32697

題名:	人機介面中的形狀辨識及其應用 Shape recognition and its application in human-computer interaction
作者:	鄭聖耀 Cheng, Sheng Yao
貢獻者:	廖文宏 Liao, Wen Hung 鄭聖耀 Cheng, Sheng Yao
關鍵詞:	人機介面形狀辨識手勢辨識
日期:	2007
上傳時間:	2009-09-17 14:04:50 (UTC+8)
摘要:	電腦硬體的發展日新月異，電腦在運算的能力有長足的提升，遠遠超過一般人腦的計算能力，隨著電腦的普及率大幅提高，電腦由以往為專業領域的工具轉變為家庭不可或缺的商品，一般大眾也成為電腦的使用者，與電腦溝通的技術（即人機介面）逐漸重要。在多樣人機介面當中，自然的人機介面尤為重要，在手持式計算裝置及平版電腦上的手寫或手勢是人機介面中較自然的方式，因此本論文將對手寫軌跡以及手勢辨識進行研究。由於此類的人機介面自由度較高，我們利用傅立葉描述元(Fourier descriptor)以及shape context，皆為平移、旋轉、縮放等rigid transformation下維持不變的方法。在手繪圖形，我們收集114位使用者的手繪資料，繪圖的過程中，依使用者直觀的方式，繪圖於電腦的觸控板，而這些使用者幾乎為首次使用觸控筆。當我們利用傅立葉描述元時，可達到67%辨識率；而使用shape context時，有90%的準確率。另外，我們將此技術應用於手勢辨識，收集348張手勢的照片，同樣使用傅立葉描述元以及shape context，其辨識率各為62%以及70%。由於我們可以利用以上二方法定義出距離，即可使用K-Nearest Neighbor(KNN)為分類的方法。分別透過傅立葉描述元以及shape context所定義的距離，在辨識3D幾何物件約可達75%與95%，而在手勢辨識約有78%以及82%的辨識率。 The cost of computing devices has dropped significantly in recent years, enabling diversified applications that require natural man-machine interaction such as pen-based computing and gesture-based communication. Whereas the automatic recognition of handwriting has been studied quite extensively, research on hand-drawn geometric shapes has received relatively little attention. In this thesis, we investigate an effective method to recognize hand-drawn geometric shapes and hand gesture. Due to the high degree of freedom of natural human-computer interface, we apply two methods, namely, Fourier descriptor (FD) and shape context (SC) to aid shape recognition. For hand-drawn shapes, we collect 114 users` free-hand drawings using Tablet PC. In this study, we achieve an accuracy of 67% by FD and 90% by SC. For gesture-based interface, we gather 348 pictures of hand gestures and obtain a classification rate of 62% by FD and 70% by SC. Since FD and SC are distance measures, we can use K-Nearest Neighbor (KNN) classifier to improve the recognition rate. The incorporation of KNN classifier has increased the precision to 75% and 95%, where distance is measured by FD and SC respectively. For hand gestures, the improved accuracy is 78% by FD and 82% by SC.
參考文獻:	【1】 S. Belongie, J. Malik, and J. Puzicha, “Shape Matching and Object Recognition Using Shape Contexts,” IEEE Transactions on Pattern Analysis and Machine Intelligence, pp.509-522, 2002. 【2】 J. O. Wobbrock, A. D. Wilson and Y. Li, “Gestures without Libraries, Toolkits or Training: A $1 Recognizer for Use Interface Prototypes,” Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology, pp. 159-168, 2007. 【3】 B. Paulson and T. Hammond, “PaleoSketch: Accurate Primitive Sketch Recognition and Beautification,” International Conference on the Intelligent User Interface, 2008. 【4】 B. Paulson and T. Hammond, “MARQS: Retrieving Sketch Using Domain- and Independent Features Learned from A Single Example using A Dual- Classifier,” Proceedings of International Workshop on Usability of User Interfaces: From Monomodal to Multimodal, 2007. 【5】 R. C. Gonzalez and R. E. Woods, “Representation and Description,” Digital Image Processing, 2nd ed., Prentice Hall, New Jersey, pp. 655-659, 2001. 【6】 D. Zhang and G. Lu, “Content based Shape Retrieval Using Different Shape Descriptors: A Comparative Study,” IEEE International Conference on Multimedia and Expo, pp. 317-320, 2001. 【7】 B. Hussain and M.R. Kabuka, “A novel feature recognition neural network and its application tocharacter recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, pp.98-106, 1994. 【8】 F. L. Bookstein, “Principal Warps: Thin-Plate Splines and the Decomposition of Deformations,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 6, pp. 567-585, 1989. 【9】 Microsoft Windows XP Tablet PC Edition Software Development Kit 1.7, http://www.microsoft.com/downloads/details.aspx?FamilyId=B46D4B83-A821-40BC-AA85-C9EE3D6E9699&displaylang=en[retrieved July 2008]. 【10】蒙恬科技，http://www.penpower.com.tw/ [retrieved July 2008]. 【11】 A. Forsberg, M. Dieterich, and R. Zeleznik, “The Music Notepad,” Proceedings of the 11th Annual ACM Symposium on User Interface Software and Technology, 1998. 【12】 J. J. LaCiola and R. C. Zeleznik, “MathPad2: a system for the creation and exploration of mathematical sketches,” ACM Transactions on Graphics, pp.38-48, 2004. 【13】 T. M. Sezgin, “Feature Point Detection and Curve Approximation for Early Processing of Free-Hand Sketches,” Master`s Thesis, Massachusetts Institute of Technology, May 2001. 【14】 P. L. Rosin, “Techniques for Assessing Polygonal Approximations of Curves,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 6, pp. 659-666, June 1997. 【15】 Y. Rubner, C. Tomasi and L. J. Guibas, “A Metric for Distributions with Applications to Image Databases,” Proceedings of the IEEE International Conference on Computer Vision, Bombay, India, pp.59-66, 1998. 【16】紀煜豪，「不同光源環境下的即時膚色辨識」，國立政治大學資訊科學所，民國96年。【17】 S. Mitra and T. Acharya, “Gesture Recognition: A Survey.” IEEE Transactions on Systems, Man, and Cybernetics, vol. 37, no. 3, pp. 311-324, May 2007.
描述:	碩士國立政治大學資訊科學學系 95753017 96
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0095753017
資料類型:	thesis
顯示於類別:	[資訊科學系] 學位論文

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