A traverse inspection system for high precision visual on-loom fabric defect detection-Reference-Cited by-同舟云学术

A traverse inspection system for high precision visual on-loom fabric defect detection

Published:2014-03-04 Issue:6 Volume:25 Page:1585-1599
ISSN:0932-8092
Container-title:Machine Vision and Applications
language:en
Short-container-title:Machine Vision and Applications

Author:

Schneider Dorian,Holtermann Timm,Merhof Dorit

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Link

http://link.springer.com/content/pdf/10.1007/s00138-014-0600-y.pdf

Reference37 articles.

1. Riethmueller, C.: Thoughts on fabric inspection on the weaving machine. Melliand Text. Engl. 81, 196–E197 (2000)

2. Mak, K., Peng, P., Lau, H.: A real-time computer vision system for detecting defects in textile fabrics. In: Industrial Technology, 2005. ICIT 2005. IEEE International Conference on 2005, 469–474 (2005)

3. Lecture Notes in Computer Science;H Castilho,2007

4. Sari-Sarraf, H., Goddard, J.S.: Vision system for on-loom fabric inspection. Ind. Appl. IEEE Trans. 35(66), 1252–1259 (1999)

5. Stojanovic, R., Mitropulos, P., Koulamas, C., Karayiannis, Y., Koubias, S., Papadopoulos, G.: Real-time vision-based system for textile fabric inspection. Real-Time Imaging 7(6), 507–518 (2001)

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