Implementation of binary and gray-scale mathematical morphology on the CNN universal machine

A cellular neural network(CNN)-based morphological engine is proposed. An effective implementation method of binary and gray-scale erosion, dilation, and reconstruction is introduced. The binary morphological operators are successfully implemented on an actual CNN universal chip. Experimental result...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems. 1, Fundamental theory and applications Fundamental theory and applications, 1998-02, Vol.45 (2), p.163-168
Main Authors: Zarandy, A., Stoffels, A., Roska, T., Chua, L.O.
Format: Article
Language:English
Subjects:
Applied sciences
Cellular neural networks
Electric, optical and optoelectronic circuits
Electronics
Engines
Exact sciences and technology
Gray-scale
Image reconstruction
Integrated circuit interconnections
Laboratories
Morphology
Neural networks
Optical devices
Optical feedback
Turing machines
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Summary:A cellular neural network(CNN)-based morphological engine is proposed. An effective implementation method of binary and gray-scale erosion, dilation, and reconstruction is introduced. The binary morphological operators are successfully implemented on an actual CNN universal chip. Experimental results are shown.
ISSN:1057-7122
1558-1268
DOI:10.1109/81.661683