The Application of Principal Component Analysis to Boost The Performance of The Automated Fabric Fault Detector And Classifier

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24410%2F14%3A%230003698" target="_blank" >RIV/46747885:24410/14:#0003698 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.fibtex.lodz.pl/2014/4/51.pdf" target="_blank" >http://www.fibtex.lodz.pl/2014/4/51.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

The Application of Principal Component Analysis to Boost The Performance of The Automated Fabric Fault Detector And Classifier

Popis výsledku v původním jazyce

There is a growing need to replace visual fabric inspection with automated systems that detect and classify fabric defects. The digital processing of fabric images utilises different methods that offer a large set of image features. The correlation between those features lead to problems during fabric fault classification and reduces the performance of the classifiers. This work extracted a combination of statistical (spatial) and Fourier transform (spectral) features from fabric images of the most frequent faults. Principal component analysis (PCA) was implemented to reduce the dimensionality of the input feature dataset, which achieved a reduction to 36% of the original data size while preserving 99% of information in the original dataset. The features processed using the PCA were fed to an artificial neural network (ANN) to classify the fault categories and then compared to another ANN that worked with the whole feature dataset. The performance of the network that was implemented af

Název v anglickém jazyce

The Application of Principal Component Analysis to Boost The Performance of The Automated Fabric Fault Detector And Classifier

Popis výsledku anglicky

There is a growing need to replace visual fabric inspection with automated systems that detect and classify fabric defects. The digital processing of fabric images utilises different methods that offer a large set of image features. The correlation between those features lead to problems during fabric fault classification and reduces the performance of the classifiers. This work extracted a combination of statistical (spatial) and Fourier transform (spectral) features from fabric images of the most frequent faults. Principal component analysis (PCA) was implemented to reduce the dimensionality of the input feature dataset, which achieved a reduction to 36% of the original data size while preserving 99% of information in the original dataset. The features processed using the PCA were fed to an artificial neural network (ANN) to classify the fault categories and then compared to another ANN that worked with the whole feature dataset. The performance of the network that was implemented af

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JS - Řízení spolehlivosti a kvality, zkušebnictví

OECD FORD obor

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Projekt

<a href="/cs/project/EE2.3.30.0065" target="_blank" >EE2.3.30.0065: Podpora tvorby excelentních výzkumných a vývojových týmů na Technické univerzitě v Liberci</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název periodika

Fibres & Textiles in Eastern Europe

ISSN

1230-3666

e-ISSN

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Svazek periodika

22

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

PL - Polská republika

Počet stran výsledku

7

Strana od-do

51-57

Kód UT WoS článku

000338825300008

EID výsledku v databázi Scopus

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