A Hybrid Machine Learning Approach to Fabric Defect Detection and Classification-Reference-Cited by-同舟云学术

A Hybrid Machine Learning Approach to Fabric Defect Detection and Classification

Published:2022 Issue: Volume: Page:135-147
ISSN:1867-8211
Container-title:Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
language:
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Author:

Mohammed Swash Sami^ORCID,Clarke Hülya Gökalp^ORCID

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-01984-5_11

Reference22 articles.

1. Standard Test Methods for Visually Inspecting and Grading Fabrics. D5430–13 (2017)

2. İzbudak, H., Alkan, A.: Denim fabric defect detection by using correlation method. In: National Conference on Electrical, Electronics and Computer Engineering, Bursa, Turkey (2010)

3. Dorrity, J.L., Vachtsevanos, G., Jasper, W.: Real-time fabric defect detection and control in weaving processes. Natl. Textile Center Ann. Report G94-2 (1996)

4. Hanbay, K., Talub, M.F., Özgüven, Ö.F.: Fabric defect detection systems and methods—a systematic literature review. Optik 127, 11960–11973 (2016)

5. Zhang, Y.F., Bresee, R.R.: Fabric defect detection and classification using image analysis. Text. Res. J. 65(1), 1–9 (1995)

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