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.
Distributed belief revision for adaptive image processing regulation
REGAZZONI, CARLO
1992-01-01
Abstract
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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