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Application of Principal Component Analysis to Boost the Performance of an Automated Fabric Fault Detector and Classifier

Research and development

Authors:

  • Eldessouki Mohamed
    Department of Textile Engineering, Faculty of Engineering, Mansoura University, Mansoura, Egypt
  • Hassan Mounir
    Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia
  • Hassan Mounir
    Department of Textile Engineering, Faculty of Engineering, Mansoura University, Mansoura, Egypt
  • Qashqary Khadijah
    Department of Fashion Design, Faculty of Art & Design, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia
  • Shady Ebraheem
    Department of Textile Engineering, Faculty of Engineering, Mansoura University, Mansoura, Egypt

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Abstract:

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 after application of the PCA increased to 90% of the correct classification rate as compared to 73.3% for the other network.

Tags:

fabric fault detector, image processing, artificial neural networks, principal component analysis.

Citation:

Eldessouki M, Hassan M, Qashqari K, Shady E. Application of Principal Component Analysis to Boost the Performance of an Automated Fabric Fault Detector and Classifier. FIBRES & TEXTILES in Eastern Europe 2014; 22, 4(106): 51-57.

Published in issue no 4 (106) / 2014, pages 51–57.

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