This paper proposes a new approach to the problem of intelligently regulating image-processing parameters of a distributed network. The proposed approach is based on two-step probabilistic process: (a) belief updating, which consists in computing a functional cost at each node of the network and, (b) belief maximization, which depends on maximizing this functional cost by using a stochastic optimization algorithm. The architecture of an image processing system, consisting of three modules connected in a chain-like structure, is presented as an example showing the capabilities of the proposed approach. Each module is provided with a priori information about the set of parameters that manage a particular data transformation, and with evaluation criteria to judge data quality and to decide on the parameters to be adjusted. Experimental results obtained by using a digitally controlled camera and lens objective, are presented to show the validity of the proposed approach.

A belief-based approach for adaptive image processing

FORESTI, Gian Luca;
1997

Abstract

This paper proposes a new approach to the problem of intelligently regulating image-processing parameters of a distributed network. The proposed approach is based on two-step probabilistic process: (a) belief updating, which consists in computing a functional cost at each node of the network and, (b) belief maximization, which depends on maximizing this functional cost by using a stochastic optimization algorithm. The architecture of an image processing system, consisting of three modules connected in a chain-like structure, is presented as an example showing the capabilities of the proposed approach. Each module is provided with a priori information about the set of parameters that manage a particular data transformation, and with evaluation criteria to judge data quality and to decide on the parameters to be adjusted. Experimental results obtained by using a digitally controlled camera and lens objective, are presented to show the validity of the proposed approach.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11390/682447
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