Volume 8, Issue 2
Automatic Inspection of Woven Fabric Density Based on Digital Image Analysis

Junfeng Jing, Qiying Deng & Pengfei Li

Journal of Fiber Bioengineering & Informatics, 8 (2015), pp. 259-266.

Published online: 2015-08

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  • Abstract

In order to inspect woven fabric density automatically, a method combining image processing and multiscale wavelet transform is proposed in this paper. Firstly, fabric images are pre-processed by Bimodal Gaussian function histogram equalization to obtain more structure information. Secondly, fabric images are decomposed into horizontal and vertical sub-images by using wavelet filter. Thirdly, texture features are extracted from the sub-images through binarization and smooth processing. Finally, density of yarns is acquired accurately after analyzing features of warps and wefts of the fabric. The experiment results prove that the proposed algorithm is perfectly suitable for three principle weaves and the relative error of automatic inspection compared with manual inspection is less than 0.86%.

  • Keywords

Fabric Density Woven Fabric Wavelet Transform Smooth Processing Binarization

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COPYRIGHT: © Global Science Press

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@Article{JFBI-8-259, author = {}, title = {Automatic Inspection of Woven Fabric Density Based on Digital Image Analysis}, journal = {Journal of Fiber Bioengineering and Informatics}, year = {2015}, volume = {8}, number = {2}, pages = {259--266}, abstract = {In order to inspect woven fabric density automatically, a method combining image processing and multiscale wavelet transform is proposed in this paper. Firstly, fabric images are pre-processed by Bimodal Gaussian function histogram equalization to obtain more structure information. Secondly, fabric images are decomposed into horizontal and vertical sub-images by using wavelet filter. Thirdly, texture features are extracted from the sub-images through binarization and smooth processing. Finally, density of yarns is acquired accurately after analyzing features of warps and wefts of the fabric. The experiment results prove that the proposed algorithm is perfectly suitable for three principle weaves and the relative error of automatic inspection compared with manual inspection is less than 0.86%.}, issn = {2617-8699}, doi = {https://doi.org/10.3993/jfbim00122}, url = {http://global-sci.org/intro/article_detail/jfbi/4705.html} }
TY - JOUR T1 - Automatic Inspection of Woven Fabric Density Based on Digital Image Analysis JO - Journal of Fiber Bioengineering and Informatics VL - 2 SP - 259 EP - 266 PY - 2015 DA - 2015/08 SN - 8 DO - http://doi.org/10.3993/jfbim00122 UR - https://global-sci.org/intro/article_detail/jfbi/4705.html KW - Fabric Density KW - Woven Fabric KW - Wavelet Transform KW - Smooth Processing KW - Binarization AB - In order to inspect woven fabric density automatically, a method combining image processing and multiscale wavelet transform is proposed in this paper. Firstly, fabric images are pre-processed by Bimodal Gaussian function histogram equalization to obtain more structure information. Secondly, fabric images are decomposed into horizontal and vertical sub-images by using wavelet filter. Thirdly, texture features are extracted from the sub-images through binarization and smooth processing. Finally, density of yarns is acquired accurately after analyzing features of warps and wefts of the fabric. The experiment results prove that the proposed algorithm is perfectly suitable for three principle weaves and the relative error of automatic inspection compared with manual inspection is less than 0.86%.
Junfeng Jing, Qiying Deng & Pengfei Li. (2019). Automatic Inspection of Woven Fabric Density Based on Digital Image Analysis. Journal of Fiber Bioengineering and Informatics. 8 (2). 259-266. doi:10.3993/jfbim00122
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