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 |
Fingerprint
ASJC Scopus subject areas
- Electrical and Electronic Engineering
Cite this
Neural network representation and implementation of gray scale morphological operators. / Ko, Sung-Jea; Morales, Aldo.
1992 IEEE International Symposium on Circuits and Systems, ISCAS 1992. Institute of Electrical and Electronics Engineers Inc., 1992. p. 105-108 230003 (Proceedings - IEEE International Symposium on Circuits and Systems; Vol. 1).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Neural network representation and implementation of gray scale morphological operators
AU - Ko, Sung-Jea
AU - Morales, Aldo
PY - 1992/1/1
Y1 - 1992/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85067200544&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85067200544&partnerID=8YFLogxK
U2 - 10.1109/ISCAS.1992.230003
DO - 10.1109/ISCAS.1992.230003
M3 - Conference contribution
AN - SCOPUS:85067200544
T3 - Proceedings - IEEE International Symposium on Circuits and Systems
SP - 105
EP - 108
BT - 1992 IEEE International Symposium on Circuits and Systems, ISCAS 1992
PB - Institute of Electrical and Electronics Engineers Inc.
ER -