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A reinforcement learning framework for parameter control in computer vision applications

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1 Author(s)
Taylor, G.W. ; University of Waterloo

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.

Published in:

Computer and Robot Vision, 2004. Proceedings. First Canadian Conference on

Date of Conference:

17-19 May 2004