Automatic Measurement Method of Yarn Dyed Woven Fabric Density via Wavelet Transform Fusion Technique
Year: 2016
Journal of Fiber Bioengineering and Informatics, Vol. 9 (2016), Iss. 2 : pp. 115–132
Abstract
The yarn density is always considered as the fundamental structural parameter used for the quality evaluation of woven fabrics. Conventional yarn density is measured by one-side image analysis. In this paper, a novel density measurement method is developed for yarn dyed woven fabrics based on dual-side fusion technique. Firstly, both face-side and back-side image of woven fabric are acquired via a lab-used dual-side imaging system. Secondly, the affine transform is used for dual-side image alignment. Then, the color images of woven fabrics are transferred from RGB to CIE-Lab color space, the intensity information of image extracted from L component is used for the texture fusion and analysis. Subsequently, the dual- side image is merged by the self-developed Wavelet transform fusion method. Finally, the fast Fourier transform is utilized to measure yarn density of the fused image, the yarn alignment image could be reconstructed using inverse fast Fourier transform. Our experimental results show that the accuracy of density measurement by using proposed method is close to 99.44% compared with traditional method and the robustness of this new proposed method is better than that of conventional analysis methods.
You do not have full access to this article.
Already a Subscriber? Sign in as an individual or via your institution
Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.3993/jfbim00236
Journal of Fiber Bioengineering and Informatics, Vol. 9 (2016), Iss. 2 : pp. 115–132
Published online: 2016-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 18
Keywords: Density Measurement
-
Automated Density Measurement of Weft Knitted Fabrics Using Backlight Imaging
ZHANG, Jing
LIU, Shuhua
XIN, Binjie
YUAN, Zhijie
XU, Yingqi
Wuhan University Journal of Natural Sciences, Vol. 28 (2023), Iss. 6 P.508
https://doi.org/10.1051/wujns/2023286508 [Citations: 0]