@Article{JFBI-8-2,
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 = {https://global-sci.com/article/86550/automatic-inspection-of-woven-fabric-density-based-on-digital-image-analysis}
}