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1st Canadian Conference on Computer and Robot Vision (CRV'04)
A reinforcement learning framework for parameter control in computer vision applications
University of Western Ontario, London, Ontario, Canada
May 17-May 19
ISBN: 0-7695-2127-4
We propose a framework for solving the parameter selection problem for computer vision applications using reinforcement learning agents. Connectionist-based function approximation is employed to reduce the state space. Automatic determination of fuzzy membership functions is stated as a specific case of the parameter selection problem. Entropy of a fuzzy event is used as a reinforcement. We have carried out experiments to generate brightness membership functions for several images. The results show that the reinforcement learning approach is superior to an existing simulated annealing-based approach.
Index Terms:
Learning,Computer vision,Application software,State-space methods,Simulated annealing,Filters,Pattern analysis,Machine intelligence,Laboratories,Function approximation
Citation:
"A reinforcement learning framework for parameter control in computer vision applications," crv, pp.496-503, 1st Canadian Conference on Computer and Robot Vision (CRV'04), 2004
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