Skip to main content
Have a personal or library account? Click to login
Local Characterisation and Detection of Woven Fabric Texture Based on a Sparse Dictionary Cover

Local Characterisation and Detection of Woven Fabric Texture Based on a Sparse Dictionary

By: ,  ,  ,   and    
Open Access
|Sep 2022

References

  1. Zhang J, Pan R, Gao W. Automatic inspection of density in yarn-dyed fabrics by utilizing fabric light transmittance and Fourier analysis. Applied Optics 2015, 54(4): 966–972.
  2. Jeyaraj PR, Nadar ERS. Computer vision for automatic detection and classification of fabric defect employing deep learning algorithm. International Journal of Clothing Science and Technology 2019;31(4): 510–521.
  3. Zhao CF, Chen Y, Ma JC. Fabric defect detection algorithm based on PHOG and SVM. Indian Journal of Fibre & Textile Research 2020; 45(1): 123–126.
  4. Hanbay, Kazim, Ozguven, et al. Fabric defect detection systems and methods-A systematic literature review. Optik-International Journal for Light-and Electronoptic 2016;127(24): 11960–11973.
  5. Wechsler H. Texture analysis — a survey. Signal Processing 1980; 2(3): 271–282.
  6. Zhou J, Wang J. Fabric defect detection using adaptive dictionaries. Textile Research Journal 2013; 83(17): 1846–1859.
  7. Zhu JY, Wang ZY, Zhong R, et al. Dictionary based surveillance image compression. Journal of Visual Communication and Image Representation 2015; 31:225–230.
  8. Zhu NB, Tang T, Tang S, et al. A sparse representation method based on kernel and virtual samples for face recognition. Optik 2013; 124(23): 6236–6241.
  9. Zhou J, Semenovich D, Sowmya A, et al. Dictionary learning framework for fabric defect detection. Journal of the Textile Institute 2014; 105(3): 223–234.
  10. Zhu Q, Wu M, Li J, et al. Fabric defect detection via small scale over-complete basis set. Textile Research Journal 2014; 84(15): 1634–1649.
  11. Donoho DL, Tsaig Y, Drori I, et al. Sparse Solution of Underdetermined Systems of Linear Equations by Stagewise Orthogonal Matching Pursuit. IEEE Transactions on Information Theory 2012, 58(2): 1094–1121.
  12. Wu Y, Zhou J, Akankwasa NT, et al. Fabric texture representation using the stable learned discrete cosine transform dictionary. Textile Research Journal 2019; 89(3): 294–310.
  13. Zhou J, Wang J. Unsupervised fabric defect segmentation using local patch approximation. Journal of the Textile Institute 2016; 107(6): 800–809.
  14. Wang Z, Bovik AC. Universal Image Quality Index. IEEE Signal Processing Letters 2002; 9(3): 81–84.
  15. Aharon M, Elad M, Bruckstein A. K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing 2006; 54(11): 4311–4322.
DOI: https://doi.org/10.2478/ftee-2022-0020 | Journal eISSN: 2300-7354 | Journal ISSN: 1230-3666
Language: English
Page range: 33 - 40
Published on: Sep 28, 2022
Published by: Łukasiewicz Research Network-Łódź Institute of Technology
In partnership with: Paradigm Publishing Services
Publication frequency: Volume open

© 2022 Ying Wu, Ren Wang, Lin Lou, Lali Wang, Jun Wang, published by Łukasiewicz Research Network-Łódź Institute of Technology
This work is licensed under the Creative Commons Attribution 3.0 License.