Real-time textile fabric flaw inspection system using grouped sparse dictionary
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Information Systems
Link
https://link.springer.com/content/pdf/10.1007/s11554-023-01319-2.pdf
Reference28 articles.
1. Lv, W.T., Lin, Q.Q., Zhong, J.Y., Wang, C.Q., Xu, W.Q.: Research progress of image processing technology for fabric defect detection. J. Tex. Res. 42(11), 197–206 (2021). https://doi.org/10.13475/j.fzxb.20200702710
2. Divyadevi, R., Kumar, B.V.: Survey of automated fabric inspection in textile industries. In: 2019 International Conference on Computer Communication and Informatics (ICCCI), pp. 1–4 (2019). https://doi.org/10.1109/ICCCI.2019.8822180
3. Gao, G, Zhang, D., Li, C., et al.: A novel patterned fabric defect detection algorithm based on GHOG and low-rank recovery. In: 2016 IEEE 13th International Conference Signal Process (ICSP). IEEE, pp. 1118–1123 (2016). https://doi.org/10.1109/ICSP.2016.7878002
4. Li, C., Gao, G., Liu, Z., et al.: Defect detection for patterned fabric images based on GHOG and low-rank decomposition. IEEE Access. 7, 83962–83973 (2019). https://doi.org/10.1109/ACCESS.2019.2925196
5. Zhu, D.D., Pan, R.R., Gao, W.D., et al.: Yarn-dyed fabric defect detection based on autocorrelation function and GLCM. Autex Res. J. 15(3), 226–232 (2015). https://doi.org/10.1515/aut-2015-0001
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