Edge detection using a new definition of entropy

The paper describes a possible model of the human perceptive process. In this paper the relation between the entropy of an image domain and the entropy of its subdomains is explored as a uniformity predicate. Such entropy is obtained from the analysis of the image histogram associating a Gaussian distribution to the maximum frequency of grey levels. With the aim of implementing the model, we have introduced a well known technique of problem solving. The most important roles of our model are played by the evaluation function (EF) and the control strategy. So the EF is related to the ratio between the entropy of one region or zone of the picture and the entropy of the entire picture. The control strategy determines the optimal path in the quadtree so that the nodes of the optimal path have minimal entropy. The paper shows some comparisons between the method and classical edge detection techniques.

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