The development of game theory in the early 1940's by John von
Neumann was a reaction against the then dominant view that problems in
economic theory can be formulated using standard methods from optimization
theory. Indeed, most real- world economic problems typically involve
conflicting interactions among decision-making agents that cannot be
adequately captured by a single (global) objective function, thereby requiring
a different, more sophisticated treatment. Accordingly, the main point made by
game theorists is to shift the emphasis from optimality criteria to
equilibrium conditions. Iin the past there have been various attempts aimed at
formulating problems in computer vision, pattern recognition and machine
learning from a game-theoretic perspective. This talk will offer an
introduction to the basic concepts of game theory and to provide a critical
overview of its main applications in computer vision and pattern recognition.
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