@Article{JFBI-8-69,
author = {Xuejuan Kang, Mengmeng Xu and Junfeng Jing},
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}
}