1. Gesture Recognition System Speaker ： Bo – Hung Chen Adviser ： Dr. Shih – Chung Chen

2. Outline Introduction Paper Review Material Methods Results Conclusion Future Works Reference

3. Introduction What is Gesture Recognition? It is the process by which the gestures made by the user are recognized by the receiver Motivation ◦ Ranging from sign language through medical rehabilitation to virtual reality

4. Introduction Wide-ranging applications such as the following ： ◦ Developing aids for the hearing impaired ◦ Enabling very young children to interact with computers ◦ Designing techniques for forensic identification ◦ Recognizing sign language ◦ Medically monitoring patients’ emotional states or stress levels Reference ： Gesture Recognition: A Survey, Sushmita Mitra, Senior Member, IEEE, and Tinku Acharya, Senior Member, IEEE

5. Introduction Wide-ranging applications such as the following ： ◦ Lie detection ◦ Navigating and/or manipulating in virtual environments ◦ Communicating in video conferencing ◦ Distance learning/tele-teaching assistance ◦ Monitoring automobile drivers’ alertness/drowsiness levels, etc Reference ： Gesture Recognition: A Survey, Sushmita Mitra, Senior Member, IEEE, and Tinku Acharya, Senior Member, IEEE

6. Introduction Commonly type ： ◦ Morphological ◦ Moving path tracking

7. Paper Review Alphanumeric Shape Recognition of Fingertip Writing Trajectory Ruei-Tang Lin, Ming-Fang Wu Alphanumeric Shape Recognition of Fingertip Writing Trajectory Ruei-Tang Lin, Ming-Fang Wu Kun Shan University, Department of Electrical Engineering, 2010

8. Paper Review

9. Sign Language for Numbers Recognition to Speech Translation System Student : Rubie Fernando Vinas, Advisor : Dr. Shih-Chung Chen Southern Taiwan University Department of Electrical Engineering Master’s Thesis AND

10. Material Use USB camera LabVIEW2010 PC

11. Methods Image processing Skin Color Swatches Fill HoleRemove Particle Dilate Thin

12. Methods Tags

13. Methods

14. Methods Recognition rules ： ROI Extract ½ Long ROI ： Range Of Interesting

15. Methods Recognition Rules ： ROI ROI ： Range Of Interesting

16. Methods Mouse Control

17. Methods Mouse Move

18. Results Recognition Results

19. Results Problem Huge “Particle”(Item, background, etc) will not be removed In somehow,Webcam is just not good enough for recognition(too much limited)

20. Conclusion Gesture Recognition System can be use to control mouse or else human computer interface Users must be trained to get used to the camera(Distance, Angle, etc) USB webcam is not only the cheapest one, but also the most limitations one

21. Future Works Try to use another camera(But not too expensive) Fix skin color problem Try to use new tag rule

22. Reference [1] 張光寒, ”3D 台灣手語辨識系統 ”, 南台科技大學電機工程研究所, 96 年 [2] 陳為尹, ” 資料手套與影像辨識之手勢控制應用 ”, 南台科技大學生物醫學工程研究所, 99 年 [3] 洪兆欣, ” 以軌跡辨識為基礎之手勢辨識系統 ”, 國立中央大學資訊工程研究所, 95 年 [4] 吳明芳、林瑞堂， ” 指尖手寫軌跡的字形辨識 ” ，崑山科技大學電機工程系碩士論文， 2009 年。 [5] Yunli Lee, Seungki Min, HwangKyu Yang, and Keechul Jung, ” Motion Sensitive Glove-based Korean Fingerspelling Tutor” HCI Lab, School of Media, College of Information Technology, Soongsil University,2007 [6] Bui, T.D.; Nguyen, L.T. ” Recognizing Postures in Vietnamese Sign Language With MEMS Accelerometers” Sensors Journal, IEEE Volume 7, Issue 5, May 2007 Rubie Fernando Vinas, [7] Rubie Fernando Vinas, “Sign Language for Numbers Recognition to Speech Translation System ”, 南台科技大學電機工 程研究所 [8] Stan Z. Li Anil K. Jain, “Handbook of Face Recognition Second Edition”, Springer-Verlag London Limited 2011

23. Thanks for your attention!