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研究生:李政信
研究生(外文):Cheng-Hsin Li
論文名稱:透過手勢辨識之互動人機介面
論文名稱(外文):An Interactive Human-Computer Interface Through Hand Gesture Recognition
指導教授:黃銘智黃銘智引用關係
口試委員:陳立杰王鴻祥
口試日期:2013-07-31
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:創新設計研究所
學門:設計學門
學類:綜合設計學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:中文
論文頁數:43
中文關鍵詞:手勢辨識自然互動深度影像
外文關鍵詞:Hand Gesture RecognitionNatural InteractionDepth Image
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  隨著辨識技術的進化,人與機械之間的互動變得更加豐富而多元化,自然人機介面令人機互動領域增添了不少想像與願景。人與電腦的直接溝通儼然形成一種趨勢,擁有控制意義的手勢辨識則成為自然互動中最重要的領域。
  手勢辨識的研究範圍很廣,本文從自然互動的觀念出發,以深度影像分析為切入點。透過深度影像之特性,探討手部追蹤與即時手勢辨識之概念。本研究對深度攝影機所讀取之深度影像進行分析運算,建立一個可追蹤使用者指尖的辨識系統,並以此系統驗證一到五的數字手勢辨識。
  本文隨後於案例驗證中,將此手勢辨識系統應用於互動遊戲與電腦控制,證明手勢辨識於互動人機介面應用之機會與價值。

  With the evolution of recognition technology, interaction between human and machine is becoming more abundant and diversified. Definition of natural user interface was added more imagination and vision to the interactive field. Human communication with the computer directly becomes a trend. Hand gesture recognition means controlling significance could be the most important area of natural interaction.
  There are many researches of hand gesture recognition, this paper aims to implement the concept of natural interaction. The depth image is an entry point to hand gesture recognition. We explore the technologies of hand tracking and fingertips recognition for real-time interaction through the features of depth image. In this research, we analyze and compute the depth image from the depth camera. We build a hand gesture recognition system which can be tracking the motion of fingers and recognizing the number of fingers on two of user’s hands. We use this recognition system to demonstration the numbers gesture from one to five fingers.
  Then we apply this system to an interactive game and computer remote control, so that the effect and the value of an interactive human computer interface through hand gesture recognition are proved in our demonstrated examples.

中文摘要 i
英文摘要 ii
誌謝 iii
目錄 iv
表目錄 v
圖目錄 vi
第一章 緒論 1
1.1 研究動機與目的 1
1.2 研究架構   1
第二章 文獻探討 3
2.1手勢辨識 3
2.2以影像外形為基礎的手勢研究 5
2.3深度影像技術 8
2.4小結 9
第三章 基於深度影像的手勢辨識 10
3.1辨識流程  10
3.2取得深度影像 11
3.3手群集追蹤 12
3.4提取手輪廓 15
3.5手形分析  18
3.6指尖定位  23
3.7手勢辨識  28
第四章 案例驗證 30
4.1系統架設  30
4.2案例一:自然互動遊戲 30
4.3案例二:手勢控制電腦 32
第五章 結果與討論  35
5.1辨識系統討論 35
5.1.1手部追蹤 35
5.1.2指尖辨識 36
5.2案例驗證討論 37
5.2.1自然互動遊戲 37
5.2.2手勢控制電腦 37
5.3手勢設計  38
5.4系統優勢  39
第六章 結論  41
參考文獻   42

[1]王西穎、張習文、戴國忠(2007),「一種面向實時交互的變形手勢跟蹤方法」,中國科學院軟件研究所,人機交互技術與智能信息處理實驗室,北京,2007
[2]呂緯昱(2010),「具有現場膚色偵測以指尖與夾角為基礎的手勢辨識方法」,台北科技大學資訊工程系,碩士論文,台北,2010
[3]黃堯彬、黃詰琳、詹寶珠(2008),「使用溫感影像做靜態手勢辨識」,成功大學電腦與通訊工程研究所,2008年數位生活科技研討會。
[4]Argyros A. A. & Lourakis M. I. A. (2004). Real time Tracking of Multiple Skin-Colored Objects with a Possibly Moving Camera, in proc. ECCV’04, Springer-Verlag, vol. 3, pp. 368-379.
[5]Argyros A. A. & Lourakis M. I. A. (2006). Binocular Hand Tracking and Reconstruction Based on 2D Shape Matching, 18th International Conference on Pattern Recognition, pp. 207-210.
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[7]Cho, S. –J., Choi, E., Bang, W. –C., Yang, J., Sohn, J., Kim, D. Y., …Kim, S. (2006). Two-stage recognition of raw acceleration signals for 3-D gesture-understanding cellphones. Proceedings of the Tenth International Workshop on Frontiers in Handwriting Recognition.
[8]Elmezain, M., Al-Hamadi, A., Appenrodt, J., & Michaelis, B. (2008). A hidden Markov model-based continuous gesture recognition system for hand motion trajectory. 19th International Conference on Pattern Recognition 2008, pp. 1–4.
[9]Ionescu, B., Coquin, D., Lambert, P., & Buzuloiu, V. (2005). Dynamic hand gesture recognition using the skeleton of the hand. Eurasip Journal on Applied Signal Processing, 2005(13), pp. 2101-2109.
[10]Kallay, M. (1981). Convex Hull Algorithms in Higher Dimensions, Unpublished Manuscript, Dept. Mathematics Univ. Oklahoma, Oklahoma: Norman.
[11]Khan, R. Z. & Adnan, N. (2012). Comparative Study of Hand Gesture Recognition System, International Journal of Computer Applications, 50, pp. 203-213.
[12]MacQueen J. (1967). Some Methods for classification and Analysis of Multivariate Observations, Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, University of California Press, 1, pp. 281-297
[13]Maisto, M., Panella, M., Liparulo, L. & Proietti, A. (2013). An Accurate Algorithm for the Identification of Fingertips Using an RGB-D Camera. IEEE Circuits and Systems Society, 3(2), pp. 272-283.
[14]Ouchi, K., Esaka, N., Tamura, Y., Hirahara, M., & Doi, M. (2005). Magic wand: an intuitive gesture remote control for home appliances. Proceedings of International Conference on Active Media Technology, pp. 274.
[15]Pavlovic, V. I., Sharma, R. & Huang, T. S. (1997). Visual interpretation of hand gestures for human-computer interaction: A Review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7), pp. 677-695.
[16]Sanchez-Nielsen, E., Anton-Canalis, L. & Hernandez-Tejera, M. (2004). Hand gesture recognition for human-machine interaction, Journal of WSCG 2004, 12(1-3), pp. 395-402.
[17]Schlomer, T., Poppinga, B., Henze, N. & Boll, S. (2008). Gesture recognition with a wii controller. Proceeedings of the Second International Conference on Tangible and Embedded Interaction, 2008, pp. 11-14.
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