Improving human computer interaction in a classroom environment using computer vision

ABSTRACT

In this paper we discuss our use of multi-modal input to improve human computer interaction. Specifically we look at the methods used in the Intelligent Classroom to combine multiple input modes, and examine in particular the visual input modes. The Classroom provides context that improves the functioning of the visual input modes. It also determines which visual input modes are needed when. We examine a number of visual input modes to see how they fit into the general scheme, and look at how the Classroom controls their operation.

REFERENCES

• 1: Aloimonos, Y. Purposive and qualitative active vision. In Proceedings on the International Conference on Pattern Recognition, pages 346-360, 1990
• 2: David Chapman, Vision, instruction, and action, MIT Press, Cambridge, MA, 1991
• 3: Firby, R. J., Kahn, R. E., Prokopowicz, P. N., and Swain, M. J. An architecture for vision and action. In Proceedings on the International Joint Conference on Artificial Intelligence, 1995
• 4: Flachsbart, J. Gargoyle: Vision in the Intelligent Classroom. Master's thesis, University of Chicago, 1997.http://www.cs.uchicago.edu/-joshfficial~content /Papers.html
• 5: Franklin, D., and Flachsbart, J. All gadget and no representation makes Jack a dull environment. Technical Report SS-98-02, American Association for Artificial Intelligence, 1998.
• 6: David Franklin, Cooperating with people: the intelligent classroom, Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence, p.555-560, July 1998, Madison, Wisconsin, United States
• 7: Ian D. Horswill, Specialization of perceptual processes, Massachusetts Institute of Technology, Cambridge, MA, 1993
• 8: Roger Ewing Kahn , Michael J. Swain, Perseus: an extensible vision system for human-machine interaction, 1996
• 9: Pinhanez, C., and Bobick, A. Intelligent Studios: Using computer vision to control TV cameras. IJCAZ Workshop on Entertainment and AUAZife, April 1995.
• 10: Saund, E. 1996. Machine interpretation of diagrammatic user interfaces to office whiteboard appliances. Technical Report, Xerox PARC, 1996.
• 11: Thomas M. Strat, Natural object recognition, Springer-Verlag New York, Inc., New York, NY, 1992
• 12: Glenn Wasson , David Kortenkamp , Eric Huber, Integrating active perception with an autonomous robot architecture, Proceedings of the second international conference on Autonomous agents, p.325-331, May 10-13, 1998, Minneapolis, Minnesota, United States [doi>10.1145/280765.280850]
• 13: Wren, C., Azarbayejani, A., Darrell, T., and Pentland A. Pfinder: Real time tracking of the human body. Media Lab Tech Report 353, Massachusetts Institute of Technology, 1995.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.5 PATTERN RECOGNITION

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE

Keywords:
computer vision, context-based vision, intelligent environments, multi-modal input