Visual Attention Using Game Theory

  • Ola Ramström
  • Henrik I. Christensen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2525)


A system using visual information to interact with its environment, e.g. a robot, needs to process an enormous amount of data. To ensure that the visual process has tractable complexity visual attention plays an important role.

A visual process will always have a number of implicit and explicit tasks that defines its purpose. The present document discusses attention mechanisms for selection of visual input to respond to the current set of tasks. To provide a truly distributed approach to attention it is suggested to model the control using game theory, in particular coalition games.


Game Theory Visual Attention Visual Process Competitive Equilibrium Blue Area 
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  1. 1.
    J. Duncan. Selective attention and the organisation of visual information. Journal of Experimental Psychology, 113(4):501–517, 1984.CrossRefGoogle Scholar
  2. 2.
    C. W. Eriksen and J. D. St. James. Visual attention within and around the field of focal attention: A zoom lens model. Perception and Psychophysics, 40(4):225–240, 1986.CrossRefGoogle Scholar
  3. 3.
    Drew Fudenberg and Jean Tirole. Game Theory. MIT Press, Cambridge, MA, 6th edition, 1998.Google Scholar
  4. 4.
    C. Koch and S. Ullman Shifts in selective visual attention: towards the underlying neural circuitry Human Neurobiology, 4:219–227, 1985.Google Scholar
  5. 5.
    M.J. Osborne and A. Rubinstein. A course in game theory. 1999.Google Scholar
  6. 6.
    Stephen E. Palmer. Vision Science: Photons to Phenomology. MIT Press, 1999.Google Scholar
  7. 7.
    M. I. Posner. Chronometric explanations of the mind. Erlbaum, Hillsdale, NJ, 1978.Google Scholar
  8. 8.
    Z. W. Pylyshyn and R. W. Storm. Tracking multiple independent targets: evidence of parallel tracking mechanisms. Spatial Vision, 3(3):179–197, 1988.CrossRefGoogle Scholar
  9. 9.
    A. Treisman and G. Gelade. A feature integration theory of attention. Cognitive Psychology, 12:97–136, 1980.CrossRefGoogle Scholar
  10. 10.
    J.K. Tsotsos. Analyzing vision at the complexity level. Behav.Br ain Sci., 13(3):423–469, 1990.CrossRefGoogle Scholar
  11. 11.
    J.K. Tsotsos, S. Culhane, W. Wai, Y. Lai, N. Davis, and F. Nuflo. Modeling visual attention via selective tuning. Artificial Intelligence, 78(1-2):507–547, 1995.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Ola Ramström
    • 1
  • Henrik I. Christensen
    • 1
  1. 1.Computational Vision and Active Perception Numerical Analysis and Computer ScienceRoyal Institute of TechnologyStockholmSweden

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