Abstract
In this paper we introduce a neural network implementation of gray scale operators. In this structure, synaptic weights are represented by a gray scale structuring element. Two learning algorithms are used to train the fuzzy morphological neural networks. The first algorithm utilizes the overall equality index. The second algorithm is based on the averaged least-mean square. It is shown that the LMS based algorithm is simpler and more robust.
| Original language | English |
|---|---|
| Title of host publication | 1992 IEEE International Symposium on Circuits and Systems, ISCAS 1992 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 105-108 |
| Number of pages | 4 |
| ISBN (Electronic) | 0780305930 |
| DOIs | |
| Publication status | Published - 1992 Jan 1 |
| Externally published | Yes |
| Event | 1992 IEEE International Symposium on Circuits and Systems, ISCAS 1992 - San Diego, United States Duration: 1992 May 10 → 1992 May 13 |
Publication series
| Name | Proceedings - IEEE International Symposium on Circuits and Systems |
|---|---|
| Volume | 1 |
| ISSN (Print) | 0271-4310 |
Conference
| Conference | 1992 IEEE International Symposium on Circuits and Systems, ISCAS 1992 |
|---|---|
| Country | United States |
| City | San Diego |
| Period | 92/5/10 → 92/5/13 |
ASJC Scopus subject areas
- Electrical and Electronic Engineering
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Cite this
Ko, S-J., & Morales, A. (1992). Neural network representation and implementation of gray scale morphological operators. In 1992 IEEE International Symposium on Circuits and Systems, ISCAS 1992 (pp. 105-108). [230003] (Proceedings - IEEE International Symposium on Circuits and Systems; Vol. 1). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISCAS.1992.230003