Multistable cellular neural networks and their application to image decomposition

Traditional cellular neural networks are arrays of coupled processing cells, where every uncoupled cell has two stationary stable states. In this paper an oscillatory function is used to define a cellular neural network whose uncoupled cells have a number of stable stationary states larger than two,...

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Bibliographic Details
Published in2009 52nd IEEE International Midwest Symposium on Circuits and Systems pp. 873 - 876
Main Authors Medina Hernandez, J.A., Gomez Castaeda, F., Moreno Cadenas, J.A.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2009
Subjects
Cellular neural networks
CNN
Image decomposition
Image processing
Image segmentation
K-means algorithm
Mathematics
Nonlinear dynamical systems
Nonlinear systems
phase line
Physics
Piecewise linear techniques
stable state
Stationary state
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ISBN1424444799
9781424444793
ISSN1548-3746
DOI10.1109/MWSCAS.2009.5235905

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Summary:Traditional cellular neural networks are arrays of coupled processing cells, where every uncoupled cell has two stationary stable states. In this paper an oscillatory function is used to define a cellular neural network whose uncoupled cells have a number of stable stationary states larger than two, so the output image has three or more grey levels. The proposed dynamics is applied to the decomposition of images with multiple gray levels.
ISBN:1424444799
9781424444793
ISSN:1548-3746
DOI:10.1109/MWSCAS.2009.5235905