Neural network representation and implementation of gray scale morphological operators
Sung-Jea Ko, Aldo W. Morales · 2003
A neural network implementation of gray-scale operators is introduced. It is based on fuzzy set theory. In this structure, synaptic weights are represented by a gray-scale structuring element. Two learning algorithms are used to train the networks. The first algorithm utilizes the overall equality index. The second algorithm is based on the averaged least-mean square (LMS). It is shown that the LMS-based algorithm is simpler and more robust.>