A Fuzzy Morphological Neural Principles and Implementation 


Vol. 3,  No. 3, pp. 449-459, Apr.  1996
10.3745/KIPSTE.1996.3.3.449


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  Abstract

The main goal of this paper is to introduce a novel definition for fuzzy mathematical morphology and a neural network implementation. The generalized-mean operator plays the key role for the definition. Such definition is well suited for neural network implementation. The first stage of the shared-weight neural network has adequate architecture to perform morphological operation. The shared-weight network performs classification based on the features extracted with the fuzzy morphological operation defined in this paper. Therefore, the parameters for the fuzzy definition can be optimized using neural network learning paradigm. Learning rules for the structuring elements, degree of membership, and weighting factors are precisely described. In application to handwritten digit recognition problem, the fuzzy morphological shared-weight neural network produced the results which are comparable to the state-of-art for this problem.

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[IEEE Style]

W. Y. Gwan and L. B. Ho, "A Fuzzy Morphological Neural Principles and Implementation," The Transactions of the Korea Information Processing Society (1994 ~ 2000), vol. 3, no. 3, pp. 449-459, 1996. DOI: 10.3745/KIPSTE.1996.3.3.449.

[ACM Style]

Won Yong Gwan and Lee Bae Ho. 1996. A Fuzzy Morphological Neural Principles and Implementation. The Transactions of the Korea Information Processing Society (1994 ~ 2000), 3, 3, (1996), 449-459. DOI: 10.3745/KIPSTE.1996.3.3.449.

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