This document discusses computer vision and its uses for interactive computer graphics. Computer vision involves acquiring, processing, analyzing and understanding images from the real world in order to produce information. It allows computers to interpret user movements and gestures through algorithms like tracking, shape recognition and motion analysis. These visual algorithms enable interactive applications where the computer can track and respond to both large and small objects in real-time, creating new possibilities for human-computer interaction. Examples provided demonstrate how computer vision can be used to track a user's hand gestures to control a television interface.

1. Name: Alam Shah
ID:10-17685-3
Course Instructor:
KHAN, MD. AL-FARABI

2. Computer Vision for Interactive
Computer Graphics
A thesis work done by the
authors-
M. Roth, K. Tanaka, C.
Weissman, W. Yerazunis
TR99-02 January 1999

3. What is computer Vision?
Computer vision is a field that includes
methods for acquiring, processing,
analyzing and understanding images and
high-dimensional data from the real world
in order to produce numerical or symbolic
information

4. Why we use computer Vision?
• Human-computer interaction
• Computers interpret user movements, gestures
and glances via fundamental visual algorithms.
• Visual algorithms: tracking, shape recognition
and motion analysis
• Interactive apps : response time is fast,
algorithms work for different subject and
environment and economical.

5. Tracking Objects
• Different methods and techniques are
used to track objects from the real world
• Interactive applications can track two
types of objects –
1. Large objects
2. Small objects

6. Large Object Tracking
• Large objects like
hand or body tracked.
• Object is in front of
camera.
• Image properties
(Image moments),
and artificial retina
chip do the trick.

7. Small Object Tracking
• Large objects tracking
techniques not
adequate.
• Track small objects
through template
based technique –
normalized correlation

8. Normalized Correlation
• Examine the fit of an object template to every position in
the analyzed image.
• The Location of maximum correlation gives the position
of the candidate hand.
• The value of that correlation indicates how likely the
image region is to be a hand.

9. Example : Television Remote
• To turn on the television, the user holds up his
hand.
• A graphical hand icon with sliders and buttons
appears on the graphics display.
• Move hand to control the hand icon

10. Conclusion
• Simple vision algorithms with restrictive
interactivity allows human-computer
interaction possible.
• Advances in algorithms and availability of
low-cost hardware will make interactive
human-computer interactions possible in
everyday life.

11. References
[1] R. Bajcsy. Active perception. IEEE Proceedings, 76(8):996-1006, 1988.
[2] A. Blake and M. Isard. 3D position, attitude and shape input using video tracking of
hands and lips. In Proc. SIGGRAPH 94,pages 185{192, 1994. In Computer
Graphics, Annual Conference Series.
[3] T. Darrell, P. Maes, B. Blumberg, and A. P.Pentland. Situated vision and behavior for
interactive environments. Technical Report 261, M.I.T. Media Laboratory, Perceptual
Computing Group, 20 Ames St., Cambridge, MA 02139, 1994.
[4] I. Essa, editor. International Workshop on Automatic Face- and Gesture-
Recognition.IEEE Computer Society, Killington, Vermont, 1997.
[5] W. T. Freeman and M. Roth. Orientation histograms for hand gesture recognition. In
M. Bichsel, editor, Intl. Workshop on automatic face and gesture-recognition, Zurich,
Switzerland, 1995. Dept. of Computer Science, University of Zurich, CH-8057.
[6] W. T. Freeman and C. Weissman. Television control by hand gestures. In M. Bichsel,
editor, Intl. Workshop on automatic face and gesture recognition, Zurich, Switzerland,
1995. Dept. of Computer Science, University of Zurich, CH-8057.

12. End of the Presentation
Thank You