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Fibers 2018, 6(4), 73; https://doi.org/10.3390/fib6040073

Applying Image Processing to the Textile Grading of Fleece Based on Pilling Assessment

Department of Industrial Engineering & Management, National Chin-Yi University of Technology, Taichung 411, Taiwan
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Received: 3 August 2018 / Revised: 11 September 2018 / Accepted: 26 September 2018 / Published: 28 September 2018
(This article belongs to the Special Issue Smart Coatings on Fibers and Textiles)
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Abstract

Textile pilling causes an undesirable appearance on the surface of garments, which is a long-standing problem. In this study, textile grading of fleece based on pilling assessment was performed using image processing and machine learning methods. Two image processing methods were used. The first method involved using the discrete Fourier transform combined with Gaussian filtering, and the second method involved using the Daubechies wavelet. Furthermore, binarization was used to segment the textile pilling from the background. Morphological and topological image processing methods were applied to extract the essential characteristics of textile image information to establish a database for the textile. Finally, machine learning methods, namely the artificial neural network (ANN) and the support vector machine (SVM), were used to objectively solve the textile grading problem. When the Fourier-Gaussian method was used, the classification accuracies of the ANN and SVM were 96.6% and 95.3%, and the overall accuracies of the Daubechies wavelet were 96.3% and 90.9%, respectively. View Full-Text
Keywords: textile; pilling; image processing; machine learning textile; pilling; image processing; machine learning
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Huang, M.-L.; Fu, C.-C. Applying Image Processing to the Textile Grading of Fleece Based on Pilling Assessment. Fibers 2018, 6, 73.

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