Abstract
This paper presents a novel approach for visual inspection of textures. The approach applies the artificial immune theory to learning the filters for texture flaw detection, which are invariant to changes of texture orientations and scales. In this paper, defect textures and defect-free textures are regarded as non-self and self respectively, and texture filters are regarded as antibodies. The clonal selection based algorithm is presented to evolve antibodies. Experimental results on TILDA textile images were done to show the feasibility of the proposed method.
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De Castro, L.N., Von Zuben, F.J.: The Clonal Selection Algorithm with Engineering Applications. In: Proc. of GECCO 2000, pp. 36–37 (2000)
Mamic, G., Bennamoun, M.: Automatic Flaw Detection in Textiles Using a Neyman-Pearson Detector. In: Proceedings of International Conference on Pattern Recognition, vol. 4, pp. 767–770 (2000)
Bodnarova, A., Bennamoun, M., Latham, S.J.: Textiles Flaw Detection Using Optimal Gabor Filter. In: Proceedings of International Conference on Pattern Recognition, vol. 4, pp. 799–802 (2000)
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© 2005 Springer-Verlag Berlin Heidelberg
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Zheng, H., Pan, L. (2005). Texture Surface Inspection: An Artificial Immune Approach. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3612. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539902_115
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DOI: https://doi.org/10.1007/11539902_115
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28320-1
Online ISBN: 978-3-540-31863-7
eBook Packages: Computer ScienceComputer Science (R0)