Efficient neural network implementation of morphological operations

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Abstract
This paper introduces a neural network implementation of gray scale operators. In this structure, synaptic weights are represented by a gray scale structuring element and trained by a learning algorithm based on an optimal criterion called the overall equality index. The proposed algorithm leads to a computationally simple implementation, with numerical examples to illustrate its performance.

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