| 摘要(英) |
The main purpose of this thesis is the design of a low cost hand gesture system to allow users control over any given hardware through simple commands without the need of any additional input devices. The system is capable of recognizing gestures made by the user using the back of their left hand, currently supported gestures are numerals 0 through 9. The system can be used in a wide range of applications such as interactive games, television channel selectors and simple control over electrical home appliances.
The methods available to the field of pattern recognition are numerous, there are many different ways of handling the initial sampling and the following classification stages. However, the range of methods shrinks considerably under the constraints of low cost hardware and real-time recognition. The hand gesture recognition system we propose here can be divided into four stages.
First is setting up the operating mode of the image sensor (the relevant settings are saved into EEPROM and are written into the sensor on boot-up). Secondly these settings are used to generate a binary image from the raw image sensor data, using YUV thresholds to discriminate between skin and background colors. Now several properties of hand gestures are used to find the center and size of the palm, after which the hand image is normalized into a 16 by 8 grid. Finally, after making some last corrections to the grid data, it is send into a fuzzy logic system that determines the type of hand gesture made. The results are shown to the user on a seven segment display. |
| 參考文獻 |
[1] F. Du, Human Skin Detection Using Color Segmentation. URL:https://courseware.vt.edu/users/abbott/5554/SkinReport.pdf, 2000.
[2] C. Garcia, G. Zikos, G. Tziritas, “Face Detection in Color Images using Wavelet Packet Analysis,” in Proc. 6th IEEE International Conference on Multimedia Computing and Systems, pp. 603-708, 1999.
[3] D.P. Huttenlocher, G.A.. Klandeman and W.J. Rucklidge “Comparing images using the Hausdorff distance,” IEEE Trans. on PAM I, vol.15, no. 9, pp. 850-863, 1993.
[4] R.C.K. Hua, L.C.D. Silva and P. Vadakkepat, “Detection and tracking of faces in real environments,” in Proc. 6th Intern. Conf. Imaging Science, Systems, and Technology (CTSST02), 2002, pp. 24-27.
[5] X.D. Huang, Hidden Markov models for speech recognition. Edinburgh University Press, Edinburgh, Scotland, United Kingdom, 1990.
[6] B. Martinkauppi, M. Laaksonen, and M. Sorian. “Behavior of skin color under varying ilhumination seen by different cameras at different color spaces,” Machine Vision Applications in Industrial Inspection IX, Martin Hunt, Editor, Proceedings of SPIE vol. 4301 pp. 102-112, 2001.
[7] L. R. Rabiner “A tutorial on hidden Markov models and selected applications in speech recognition,” IEEE Trans. vol. 77, No. 2, pp.257-286, 1989.
[8] http://www.cyberon.com.tw 賽微科技股份有限公司。
[9] http://www.penpower.com.tw 蒙恬科技股份有限公司。
[10] 王國強, An Eye-Tracking System and Its Application in Human Computer Interfaces. 國立中央大學資訊工程學系碩士論文, 2003
[11] 王國榮, 基於資料手套的智慧型手勢辨識之廣泛研究. 國立台灣科技大學電機工程學系碩士論文, 2001.
[12] 刘江华 “基於光流的動態手勢識別,” Computer Engineering. vol. 28, no. 4, pp. 104-105, 2002.
[13] 刘江华 “基於視覺的動態手勢識別及其在仿人機器人交互中的應用,” 機器人 ROBOT. vol. 24, no. 3, pp. 197-216, 2002.
[14] 張良囯 “基於Hausdorff距離的手勢識別”, Journal of Image and Graphics. vol. 7, no. 11, pp. 1144-1150, 2002.
[15] 連國珍, 數位影像處理. 儒林圖書有限公司, 2002
[16] 郭大正, 停車場自動監視系統. 私立中華大學資訊工程學系碩士論文, 2004
[17] 蘇木春, 張孝德, 機器學習：類神經網路、模糊系統以及基因演算法則. 全華科技圖書股份有限公司, 2002. |
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