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2007
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Associative Memory for Noisy and Structurally Deformed Two-Dimensional Images Using Neural Networks

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Associative Memory for Noisy and Structurally Deformed Two-Dimensional Images Using Neural Networks
—This paper studies the problem of understanding noisy and structurally deformed two-dimensional images by means of abstractly defined neural works. First, in the framework of systems theory a neural network defined over a Hilbert space is introduced such that any given vectors in the Hilbert space are assigned to locally asymptotically stable fixed points of the network. Then, introducing structural deformation into images a modified neural network is constructed to remove such structural deformation as well as noise. Finally, the modified neural network is used for implementing associative memory of two-dimensional images corrupted by structural deformation as well as nose, and some numerical examples are presented to illustrate the result.
Hiroshi Inaba, Tomoki Takahashi, Keylan Alimhan
Added 03 Jun 2010
Updated 03 Jun 2010
Type Conference
Year 2007
Where ICNSC
Authors Hiroshi Inaba, Tomoki Takahashi, Keylan Alimhan
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