Principal component analysis neural network hybrid classification approach for galaxies images

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F14%3A86096575" target="_blank" >RIV/61989100:27240/14:86096575 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/14:86096575

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-01781-5_21" target="_blank" >http://dx.doi.org/10.1007/978-3-319-01781-5_21</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-01781-5_21" target="_blank" >10.1007/978-3-319-01781-5_21</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Principal component analysis neural network hybrid classification approach for galaxies images

Popis výsledku v původním jazyce

This article presents an automatic hybrid approach for galaxies images classification based on principal component analysis (PCA) neural network and moment-based features extraction algorithms. The proposed approach is consisted of four phases; namely image denoising, feature extraction, reduct generation, and classification phases. For the denoising phase, noise pixels are removed from input images, then input galaxy image is normalized to a uniform scale and Hu seven invariant moment algorithm is applied to reduce the dimensionality of the feature space during the feature extraction phase. Subsequently, for reduct generation phase, attributes in the information system table that is more important to the knowledge is generated as a subset of attributes. Rough set is used as feature reduction approach. The subset of attributed, which is called a reduct, is fully characterizing the knowledge in the database. Finally, during the classification phase, principal component analysis neural n

Název v anglickém jazyce

Principal component analysis neural network hybrid classification approach for galaxies images

Popis výsledku anglicky

This article presents an automatic hybrid approach for galaxies images classification based on principal component analysis (PCA) neural network and moment-based features extraction algorithms. The proposed approach is consisted of four phases; namely image denoising, feature extraction, reduct generation, and classification phases. For the denoising phase, noise pixels are removed from input images, then input galaxy image is normalized to a uniform scale and Hu seven invariant moment algorithm is applied to reduce the dimensionality of the feature space during the feature extraction phase. Subsequently, for reduct generation phase, attributes in the information system table that is more important to the knowledge is generated as a subset of attributes. Rough set is used as feature reduction approach. The subset of attributed, which is called a reduct, is fully characterizing the knowledge in the database. Finally, during the classification phase, principal component analysis neural n

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název statě ve sborníku

Advances in Intelligent Systems and Computing. Volume 237

ISBN

978-3-319-01780-8

ISSN

2194-5357

e-ISSN

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Počet stran výsledku

13

Strana od-do

225-237

Název nakladatele

Springer

Místo vydání

Basel

Místo konání akce

Ostrava

Datum konání akce

22. 8. 2013

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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