Abstract
This work investigates the problem of automatic and robust fabric defect detection and classification which are more essential and important in assuring the fabric quality. Two characteristics of this work are: first, a new scheme combining Gabor filters and Gaussian mixture model (GMM) is proposed for fabric defect detection and classification. In detection, the foreground mask and texture features are extracted using Gabor filters. In classification, a GMM based classifier is trained and assigns each foreground pixel to known classes. The second characteristic of this work is the test data is actually collected from Qinfeng textile factory, China, including nine different fabric defects with more than 1000 samples. All the evaluation of our method is based on these actual fabric images and the experimental results show the proposed algorithm achieved satisfied performance.
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References
Kumar, A.: Computer-Vision-Based Fabric Defect Detection: A Survey. IEEE Transactions on Industrial Electronics 55, 348–363 (2008)
Kumar, A.: Neural network based detection of local textile defects. Pattern Recognition 36, 1645–1659 (2003)
Kim, S.C., Kang, T.J.: Texture classification and segmentation using wavelet packet frame and Gaussian mixture model. Pattern Recognition 40, 1207–1221 (2007)
Kumar, A., Pang, K.H.: Defect Detection in Textured Materials Using Gabor Filters. IEEE Transactions on Industry Applications 38, 425–440 (2002)
Mak, K.L., Peng, P.: Detecting Defects in Textile Fabrics with Optimal Gabor Filters. Proceedings of World Academy of Science, Engineering and Technology 13, 75–80 (2006)
Randen, T., Husoy, J.H.: Filtering for Texture Classification: A Comparative Study. IEEE Trans Pattern Anal Mach Intell 21, 291–310 (1999)
Zhang, D., Kong, W.K., You, J., Wong, M.: Online Palmprint Identification. IEEE Trans. 25(9), 1041–1050 (2003)
Bouman, C.A.: Cluster: An Unsupervised Algorithm for Modeling Gaussian Mixtures, http://www.ece.purdue.edu/~bouman.2001-10
Jain, A.K., Karu, K.: Learning Texture Discrimination Masks. IEEE Trans. on Pattern Analysis and Machine Intelligence 18, 195–205 (1996)
Kim, K.I., Jung, K., Park, S.H., Kim, H.J.: Support Vector Machines for Texture Classificaion. IEEE Trans. on Pattern Analysis and Machine Intelligence 24(11) (2002)
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Zhang, Y., Lu, Z., Li, J. (2010). Fabric Defect Detection and Classification Using Gabor Filters and Gaussian Mixture Model. In: Zha, H., Taniguchi, Ri., Maybank, S. (eds) Computer Vision – ACCV 2009. ACCV 2009. Lecture Notes in Computer Science, vol 5995. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12304-7_60
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DOI: https://doi.org/10.1007/978-3-642-12304-7_60
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12303-0
Online ISBN: 978-3-642-12304-7
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