Abstract
Due to the distribution of fabric defect shown the sparseness, it is possible to describe the fabric defects feature using sparse representation in particular transform. In this paper, we proposed a novel approach based on sparse representation for detecting patterned fabric defect. In our work, the defective fabric image is expressed by sparse representation model, it is represented as a linear superposition of three components: defect, background and noise. The defective components can be decomposed effectively by using the principle of base pursuit denoising algorithm and block coordination relaxation algorithm. The fabric defect detection is realized by analyzing the defect components. Experimental results demonstrate that the proposed approach is more efficient to detect a variety of fabric defects, in particularly the pattern fabrics.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kumar, A.: Computer-vision-based fabric defect detection: a survey. IEEE Trans. Ind. Electron. 55(1), 348–363 (2008)
Tian, C., Bu, H., Wang, J., Chen, X.: Fabric defect detection based on fractal feature of time series. J. Text. Res. 31(5), 44–48 (2010)
Selver, M.A., Avşar, V., Özdemir, H.: Textural fabric defect detection using statistical texture transformations and gradient search. J. Text. Inst. 105(9), 998–1007 (2014)
Gao, Y., Ma, J., Yuille, A.L.: Semi-supervised sparse representation based classification for face recognition with insufficient labeled samples. IEEE Trans. Image Process. 26(5), 2545–2560 (2017)
Zhao, C., Li, X., Ren, J., Marshall, S.: Improved sparse representation using adaptive spatial support for effective target detection in hyperspectral imagery. Int. J. Remote Sens. 34(24), 8669–8684 (2013)
Cao, F., Yang, Z., Ren, J., Ling, W.: Sparse representation based augmented multinomial logistic extreme learning machine with weighted composite features for spectral spatial hyperspectral image classification. In: CVPR, pp. 1–15. eprint arXiv:1709.03792, University of Maryland at College Park (2017)
Liu, L., Chen, L., Chen, C., Tang, Y., Chi, M.: Weighted joint sparse representation for removing mixed noise in image. IEEE Trans. Cybern. 1(99), 1–12 (2017)
Bridwell, D.A., Rachakonda, S., Rogers, F.S., Pearlson, G.D., Calhoun, V.D.: Spatiospectral decomposition of multi-subject EEG: evaluating blind source separation algorithms on real and realistic simulated data. Brain Topogr. 31(1), 1–15 (2018)
Georgiev, P., Theis, F., Cichocki, A.: Sparse component analysis and blind source separation of underdetermined mixtures. IEEE Trans. Neural Netw. 16(4), 992–996 (2005)
Starck, J.L., Murtagh, F., Fadili, J.: Sparse Image and Signal Processing: Wavelets, Curvelets, Morphological Diversity. Cambridge University Press, Cambridge (2010)
Elad, M.: From exact to approximate solutions. In: Elad, M. (ed.) Sparse and Redundant Representations. Springer, New York (2010). https://doi.org/10.1007/978-1-4419-7011-4_5
Sardy, S., Bruce, A.G.: Block coordinate relaxation methods for nonparamatric signal denoising. Proc. SPIE. 3391, 75–86 (1998)
Starck, J.L., Elad, M., Donoho, D.L.: Image decomposition via the combination of sparse representations and a variational approach. IEEE Trans. Image Process. 14(10), 1570–1582 (2005)
Ngan, H.Y.T., Pang, G.K.H., Yung, N.H.C.: Automated fabric defect detection, a review. Image Vis. Comput. 29(7), 442–458 (2011)
Wright, S.J., Nowak, R.D., Figueiredo, M.A.T.: Sparse reconstruction by separable approximation. IEEE Trans. Signal Process. 57(7), 2479–2493 (2009)
Elad, M., Figueiredo, M.A.T., Ma, Y.: On the role of sparse and redundant representations in image processing. Proc. IEEE 98(6), 972–982 (2010)
Yuan, X., Wu, L., Peng, Q.: An improved Otsu method using the weighted object variance for defect detection. Appl. Surf. Sci. 349, 472–484 (2015)
Zhang, Yu., Lu, Zhaoyang, Li, Jing: Fabric defect detection and classification using gabor filters and gaussian mixture model. In: Zha, Hongbin, Taniguchi, Rin-ichiro, Maybank, Stephen (eds.) ACCV 2009. LNCS, vol. 5995, pp. 635–644. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12304-7_60
Liu, S., Li, P., Zhang, L., Zhang, H., Zhang, H., Jing, J.: Defect detection based on sparse coding dictionary learning. J. Xi’an Polytech. Univ. 29(5), 594–599 (2015)
Xie, J., Zhang, L., You, J., Shiu, S.: Effective texture classification by texton encoding induced statistical features. Pattern Recognit. 48(2), 447–457 (2014)
Li, Y., Zhao, W., Pan, J.: Deformable patterned fabric defect detection with Fisher criterion-based deep learning. IEEE Trans. Autom. Sci. Eng. 14(2), 1256–1264 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Jing, JF., Ma, H., Liu, ZM. (2018). Fabric Defect Detection Based on Sparse Representation Image Decomposition. In: Ren, J., et al. Advances in Brain Inspired Cognitive Systems. BICS 2018. Lecture Notes in Computer Science(), vol 10989. Springer, Cham. https://doi.org/10.1007/978-3-030-00563-4_41
Download citation
DOI: https://doi.org/10.1007/978-3-030-00563-4_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00562-7
Online ISBN: 978-3-030-00563-4
eBook Packages: Computer ScienceComputer Science (R0)