This document summarizes a project on developing a multi-touch interactive screen system using computer vision techniques. It is supervised by professors and TAs from the computer science department of Ain Shams University. The system uses a projector, camera, and depth sensor to allow gesture-based interaction on a large display without direct touching. The document outlines the system framework, including modules for calibration, segmentation, hand detection, tracking, gesture recognition and the interface. It discusses the system's applications, limitations, and plans for future work including multi-user support and full body tracking. A final demonstration of the working system is also mentioned.

1. Faculty of computer and information science Ain Shams University Section 1 Multi-Touch Interactive Screen Touch Your Vision

2. Supervisors: Professor Dr. Mohammed Roushdy Faculty of computer and information sciences Dr. Haythem El-Messiry Faculty of computer and information sciences T.A. Ahmad Salah Faculty of computer and information sciences Sponsors:

3. Teamwork:

4. Agenda: Introduction. Interactive Screen Vs Other systems. Market Research &Customer needs. Physical Environment. System Framework. System Modules. Applications. Limitations. Future Work. Final Demo. References.

6. HCI (Human Computer Interaction) system.

7. Interact with hand gestures(shapes).

9. future without annoying input devices, and to be proud of being a part of accomplishing such a dream. develop the Interactive Screen System to be able to handle more features and gestures and to get over its limitation which will make the user get satisfied with its usability and flexibility of use.

11. Automatic hand detection. April-2010

12. Z-Depth Module. April-2010

14. Physical Environment: other alternative solutions VS proposed solution

16. Diamond Touch.

17. Touch Screens.

18. Cost

19. No need To touch the screen.

20. Gesture Recognition.

21. Dynamic Gesture Recognition.

22. Bare Hands.

24. Input Framework Calibration Segmentation System Controlling Hand Detection Multi hand Tracking Touch Detection Gesture Recognition Event Interface

25. Input apply a geometric calibration using the four calibration points acquired by the configuration module. Calibration Experimental Results Segmentation Hand Detection Multi hand Tracking Touch Detection Gesture Recognition Event Interface

26. Input It’s main task is to generate a binary image from the captured image represents the foreground Calibration Experimental Results Simple back ground Complex back ground Segmentation Hand Detection Multi hand Tracking Touch Detection Gesture Recognition Event Interface

27. Input responsible for detecting the hand position automatically at any position, with certain gesture (Open Hand) Calibration Segmentation Experimental Results Hand Detection Multi hand Tracking Touch Detection Gesture Recognition Event Interface

28. Input responsible for keep track with the user hand, know the actual position of the hand all the time Calibration Experimental Results Segmentation Hand Detection Multi hand Tracking Touch Detection Gesture Recognition Event Interface

29. Input the main task is to decide whether the user did touch the screen or not Calibration Experimental Results Segmentation Hand Detection Multi hand Tracking Touch Detection Gesture Recognition Event Interface

31. Dynamic.Calibration Experimental Results Segmentation Hand Detection Multi hand Tracking Touch Detection Gesture Recognition Event Interface

34. user must wear Long Sleeves.

36. Demo

37. References [1]. Mennat-Allah MostafaMohammad,NadaSherifAbd El Galeel,Rana Mohammad Ali Roshdy,Sarah Ismail Ibrahim, Multi Touch Interactive Surface, Faculty of Computer and Information Sciences, Ain shams University, Cairo, Egypt, 2009. [2]. Kai Briechle, Uwe D. Hanebeck, Template Matching using Fast Normalized Cross Correlation, Institute of Automatic Control Engineering,TechnischeUniversitätMünchen, 80290 München, Germany.,2001. [3]. Rafeal C. Gonzalez, Richard E. Woods, DIGITAL IMAGE PROCESSING, Third edition, Pearson, 2008. [4]. Gray Bradski, Adrian kaebler, Learning Open CV,O'Reilly Media, 2008. [5]. Alan M. McIvor, Background Subtraction Techniques In Proc. of Image and Vision Computing, Auckland, New Zealand, 2000. [6]. Francesca Gasparini, RaimondoSchettini , Skin Segmentation using multiple thresholding, Milano Italy, 2007.

38. References [7]. Hideki Koike, MasatakaToyoura, Kenji Oka and Yoichi Sato, 3-D Interaction with Wall-Sized Display, IEEE computer society,2008. [8].MahdiMirzabaki , A New Method for Depth Detection Using Interpolation Functions using single camera, INTERNATIONAL ARCHIVES OF PHOTOGRAMMETRY REMOTE SENSING AND SPATIAL INFORMATION SCIENCES, 2004, VOL 35; PART 3, pages 724-726. [9]. Patrick Horain and Mayank Bomb, 3D Model Based Gesture Acquisition Using a Single Camera,proceedings of sixth IEEE on applications of computer vision ,2002. [10] Z. Cˇernekova´, N. Nikolaidis and I. Pitas, SINGLE CAMERA POINTING GESTURE RECOGNITION USING SPATIAL FEATURES AND SUPPORT VECTOR MACHINES,EUSIPCO,Pozan,2007.

39. Any Questions Thank You