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Fabric Defect Detection Based on SRG-PCNN

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Abstract:

Defect segmentation has been a focal point in fabric inspection research, and it remains challenging because it detects delicate features of defects complicated by variations in weave textures and changes in environmental factors. According to the different features between the normal fabric image and defect image, this paper presents an adaptive image segmentation method based on a simplified region growing pulse coupled neural network (SRG-PCNN) for detecting fabric defects. The validation tests on the developed algorithms were performed with fabric images, and results showed that SRG-PCNN is a feasible and efficient method for defect detection.

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Periodical:

Advanced Materials Research (Volumes 148-149)

Pages:

1319-1326

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.148-149.1319

Citation:

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Online since:

October 2010

Authors:

Xiao Shu Si, Hong Zheng, Xue Min Hu

Keywords:

Defect Detection, Fabric, SRG-PCNN

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© 2011 Trans Tech Publications Ltd. All Rights Reserved

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References

[1] C.S. Cho, B.M. Chung and M.J. Park: Development of real-time vision-based fabric inspection system, IEEE Transactions on Industrial Electronics, pp.1073-1079 (2005).

DOI: 10.1109/tie.2005.851648

Google Scholar

[2] Ajay Kumar: Computer-Vision-Based Fabric Defect Detection: A Survey, IEEE Transactions on Industrial Electionics, pp.348-363 (2008).

DOI: 10.1109/tie.1930.896476

Google Scholar

[3] Jasper, W.J., Gamier, S.J., Potlapalli, H.: Texture characterization and defect detection using adaptive wavelets. Optical Eng. 35(11), pp.3140-3149(1996).

DOI: 10.1117/1.601054

Google Scholar

[4] Ajay Kumar, K.H. Pang: Defect Detection in Textured Materials Using Gabor Filters. IEEE Transactions on industry applications. Vol. 38(2). 425-440 (2002).

DOI: 10.1109/28.993164

Google Scholar

[5] Ma Y.D., Li L., Wang Y.F., et al: Pulse-Coupled Neural Network's Principles and Applications. Beijing: Science Press, (2006).

Google Scholar

[6] Eckhorn R, Reitboeck H J, Arndt M, et al: Feature Linking via Synchronization among Distributed Assemblies : Simulation of Results from Cat Cortex. Neural Computation, 2(3): 293-307 (1990).

DOI: 10.1162/neco.1990.2.3.293

Google Scholar

[7] Johnson, J.L., Padgett, M. L: PCNN Models and Applications. IEEE Trans. Neural Networks 10(3), 480-498(1999).

DOI: 10.1109/72.761706

Google Scholar

[8] Duan L. J, Lin Z. Q: A method of human skin region detection based on PCNN. Lecture Notes in Computer Science. pp.486-493 (2009).

DOI: 10.1007/978-3-642-01513-7_53

Google Scholar

[9] Robert D Stewart, Iris Fermin, Manfred Opper: Region Growing With Pulse-coupled Neural Networks: an alternative to seeded region growing. IEEE Trans. On Neural Network, 13(6): 1557-1562 (2002).

DOI: 10.1109/tnn.2002.804229

Google Scholar

[10] Liu, J.Z., Li, W. Q: The automatic thresholding of gray-level pictures via two-dimensional Otsu method. Acta Automat. Sinica 19(1), 101-105 (1993).

Google Scholar