Distributed Belief Revision for Adaptive Image Processing Regulation

A theoretical approach to the problem of intelligent regulation of data-processing parameters is proposed in terms of joint probability maximization. It is shown that, under suitable hypotheses, the problem can be solved by maximizing, in a distributed way, the product of computationally more tractable conditional probabilities. As a case study, the implementation of an architecture made up of four units is investigated.

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