An Empirical Study on Fabric Defect Classification Using Deep Network Models-Reference-Cited by-同舟云学术

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An Empirical Study on Fabric Defect Classification Using Deep Network Models

Published:2019 Issue: Volume: Page:739-746
ISSN:0302-9743
Container-title:Future Data and Security Engineering
language:
Short-container-title:

Author:

Anh Nguyen Thi Hong,Giao Bui Cong

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-35653-8_54

Reference22 articles.

1. Boukouvalas, C.: Color grading of randomly textured ceramic tiles using color histograms. IEEE Trans. Industr. Electron. 46(1), 219–226 (1999)

2. Chan, C.-H., Pang, G.K.H.: Fabric defect detection by Fourier analysis. IEEE Trans. Ind. Appl. 36(5), 1267–1276 (2000)

3. Han, Y., Shi, P.: An adaptive level-selecting wavelet transform for texture defect detection. Image Vis. Comput. 25(8), 1239–1248 (2007)

4. Hong Kong Productivity Council. Textile Handbook 2000. The Hong Kong Cotton Spinners Association (2000)

5. Huang, C.-C., Chen, I.-C.: Neural-fuzzy classification for fabric defects. Text. Res. J. 71(3), 220–224 (2001)

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Deep learning-based fabric defect detection: A review;Textile Research Journal;2022-10-10

2. Automatic defect detection for fabric printing using a deep convolutional neural network;International Journal of Fashion Design, Technology and Education;2021-05-12

3. Detection of Fiber Defects Using Keypoints and Deep Learning;International Journal of Pattern Recognition and Artificial Intelligence;2020-12-05

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