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Volume 8, Issue 1
Automatic Classification of Woven Fabric Structure Based on Computer Vision Techniques

Xuejuan Kang, Mengmeng Xu & Junfeng Jing

DOI: 10.3993/jfbi03201507

Journal of Fiber Bioengineering & Informatics, 8 (2015), pp. 69-79.

Published online: 2015-08

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  • Abstract
Traditionally woven fabric structure classification is based on manual work in textile industry. This paper proposes an automatic approach to classify the three woven fabrics: plain, twill and satin weave. Firstly 2-D wavelet transform is used to obtain low frequency sub-image in order to reduce the analysis of fabric images. Then graylevel co-occurrence matrix (GLCM) and Gabor wavelet are adopted to extract the texture features of pre-processing fabric images. Finally Probabilistic Neural Network (PNN) is applied to classify the three basic woven fabrics. The experimental results demonstrate that the proposed method can automatically, efficiently classify woven fabrics and obtain accurate classification results (93.33%).
  • Keywords

Woven Fabric Structure Automatic Classification 2-D Wavelet Transform GLCM Gabor Wavelet PNN

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COPYRIGHT: © Global Science Press

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@Article{JFBI-8-69, author = {}, title = {Automatic Classification of Woven Fabric Structure Based on Computer Vision Techniques}, journal = {Journal of Fiber Bioengineering and Informatics}, year = {2015}, volume = {8}, number = {1}, pages = {69--79}, abstract = {Traditionally woven fabric structure classification is based on manual work in textile industry. This paper proposes an automatic approach to classify the three woven fabrics: plain, twill and satin weave. Firstly 2-D wavelet transform is used to obtain low frequency sub-image in order to reduce the analysis of fabric images. Then graylevel co-occurrence matrix (GLCM) and Gabor wavelet are adopted to extract the texture features of pre-processing fabric images. Finally Probabilistic Neural Network (PNN) is applied to classify the three basic woven fabrics. The experimental results demonstrate that the proposed method can automatically, efficiently classify woven fabrics and obtain accurate classification results (93.33%).}, issn = {2617-8699}, doi = {https://doi.org/10.3993/jfbi03201507}, url = {http://global-sci.org/intro/article_detail/jfbi/4687.html} }
TY - JOUR T1 - Automatic Classification of Woven Fabric Structure Based on Computer Vision Techniques JO - Journal of Fiber Bioengineering and Informatics VL - 1 SP - 69 EP - 79 PY - 2015 DA - 2015/08 SN - 8 DO - http://doi.org/10.3993/jfbi03201507 UR - https://global-sci.org/intro/article_detail/jfbi/4687.html KW - Woven Fabric Structure KW - Automatic Classification KW - 2-D Wavelet Transform KW - GLCM KW - Gabor Wavelet KW - PNN AB - Traditionally woven fabric structure classification is based on manual work in textile industry. This paper proposes an automatic approach to classify the three woven fabrics: plain, twill and satin weave. Firstly 2-D wavelet transform is used to obtain low frequency sub-image in order to reduce the analysis of fabric images. Then graylevel co-occurrence matrix (GLCM) and Gabor wavelet are adopted to extract the texture features of pre-processing fabric images. Finally Probabilistic Neural Network (PNN) is applied to classify the three basic woven fabrics. The experimental results demonstrate that the proposed method can automatically, efficiently classify woven fabrics and obtain accurate classification results (93.33%).
Xuejuan Kang, Mengmeng Xu & Junfeng Jing. (2019). Automatic Classification of Woven Fabric Structure Based on Computer Vision Techniques. Journal of Fiber Bioengineering and Informatics. 8 (1). 69-79. doi:10.3993/jfbi03201507
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