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

Kód výsledku v 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>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

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

Popis výsledku anglicky

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

Druh

D - Stať ve sborníku

CEP obor

JA - Elektronika a optoelektronika, elektrotechnika

OECD FORD obor

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Projekt

<a href="/cs/project/GA102%2F03%2F0762" target="_blank" >GA102/03/0762: Analýza přenosových parametrů xDSL systémů pomocí modelů reálných přístupových sítí</a><br>

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2003

Kód důvěrnosti údajů

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

Název statě ve sborníku

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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Počet stran výsledku

5

Strana od-do

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Název nakladatele

CVUT Praha

Místo vydání

Praha

Místo konání akce

Praha

Datum konání akce

10. 9. 2003

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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