Kohenen Neural Network for Image Processing. In Proceeding of the 10th International Workshop on Systems, Signals and Image Processing

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F03%3APU40201" target="_blank" >RIV/00216305:26220/03:PU40201 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Kohenen Neural Network for Image Processing. In Proceeding of the 10th International Workshop on Systems, Signals and Image Processing

Original language description

The Kohonen network is one of the self-organizing neural networks. The number of inputs coming to neurons is equal to the number of inputs to the Kohonen network. A characteristic feature of an ART network is its ability to switch the variable and stablemode without damaging the information learned. The ART network is very sensitive to noise and failure and that is why it is not suitable for the recognition of damaged pictures. It is not necessary to solve the problem of local minimum as the ART network is always convergent to optimum solution. A real technological scene was simulated by digitizing two-dimensional pictures of real objects. The ART network gave relatively good results. Its application is suitable especially for the recognition of not very damaged and noisy pictures and in the case when other patterns are added to the learned network. The Kohonen network presented excellent results. The learning went off very quickly and that is why the Kohonen network is suitable for a

Czech name

Kohenen Neural Network for Image Processing. In Proceeding of the 10th International Workshop on Systems, Signals and Image Processing

Czech description

The Kohonen network is one of the self-organizing neural networks. The number of inputs coming to neurons is equal to the number of inputs to the Kohonen network. A characteristic feature of an ART network is its ability to switch the variable and stablemode without damaging the information learned. The ART network is very sensitive to noise and failure and that is why it is not suitable for the recognition of damaged pictures. It is not necessary to solve the problem of local minimum as the ART network is always convergent to optimum solution. A real technological scene was simulated by digitizing two-dimensional pictures of real objects. The ART network gave relatively good results. Its application is suitable especially for the recognition of not very damaged and noisy pictures and in the case when other patterns are added to the learned network. The Kohonen network presented excellent results. The learning went off very quickly and that is why the Kohonen network is suitable for a

Type

D - Article in proceedings

CEP classification

JA - Electronics and optoelectronics

OECD FORD branch

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Project

<a href="/en/project/GA102%2F03%2F0762" target="_blank" >GA102/03/0762: Analysis of xDSL Transmission parameters using computer models of real networks</a><br>

Continuities

Z - Vyzkumny zamer (s odkazem do CEZ)

Publication year

2003

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceeding of the 10th International Workshop on Systems, Signals and Image Processing

ISBN

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ISSN

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e-ISSN

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Number of pages

5

Pages from-to

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Publisher name

CVUT Praha

Place of publication

Praha

Event location

Praha

Event date

Sep 10, 2003

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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