TY - GEN
T1 - Neural network representation and implementation of gray scale morphological operators
AU - Ko, Sung Jea
AU - Morales, Aldo
N1 - Funding Information:
This work was partially supported by The University of Michigan-Dearborn and The Pennsylvania State University.
Publisher Copyright:
© 1992 IEEE.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 1992
Y1 - 1992
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.
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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.
T2 - 1992 IEEE International Symposium on Circuits and Systems, ISCAS 1992
Y2 - 10 May 1992 through 13 May 1992
ER -