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Defect Detection of Tiles Using 2D-Wavelet Transform and Statistical Features

Authors: S. A. Monadjemi, M.Ghazvini, N. Movahhedinia, K. Jamshidi

Abstract:

In this article, a method has been offered to classify normal and defective tiles using wavelet transform and artificial neural networks. The proposed algorithm calculates max and min medians as well as the standard deviation and average of detail images obtained from wavelet filters, then comes by feature vectors and attempts to classify the given tile using a Perceptron neural network with a single hidden layer. In this study along with the proposal of using median of optimum points as the basic feature and its comparison with the rest of the statistical features in the wavelet field, the relational advantages of Haar wavelet is investigated. This method has been experimented on a number of various tile designs and in average, it has been valid for over 90% of the cases. Amongst the other advantages, high speed and low calculating load are prominent.

Keywords: Neural Networks, classification, Wavelet Transform, Defect Detection, statistical features, tile and ceramic quality inspection

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1328348

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References:


[1] A. Afrasiabian, M. Jamzad, "defect detection of ceramic tile surfaces in an realtime machine vision systems," proceedings of the sixth annual conference of Iran computer association, pages 23-32, Isfahan University, Feb 2001.
[2] S.M. Nosrati,, R. Safabakhsh, "a new approach for detection of defects of tile high contrast using 2-dimensional wavele,t" proceedings of th fourth conference of Iran machine vision and image processing", Feb 2007.
[3] A. Monadjemi, B. Mirmehdi, and T. Thomas "Restructured Eignfilter Matching for Novelty Detection in Random Textures," in proceedings of the 15th british Machin Vision conference, 2004, pp. 637-646.
[4] K. L. Mak and P. Peng, "An automated inspection system for textile fabrics based on Gabor filters," Robotics and Computer-Integrated Manufacturing, vol. 24, pp. 359-369, Jun 2008.
[5] S. Kabir, P. Rivard, and G. Ballivy, "Neural-network-based damage classification of bridge infrastructure using texture analysis," Canadian Journal of Civil Engineering, vol. 35, pp. 258-267, Mar 2008.
[6] A. Latif-Amet, A. Ertuzun, and A. Ercil, "An efficient method for texture defect detection: sub-band domain co-occurrence matrices," Image and Vision Computing, vol. 18, pp. 543,-553 May 2000.
[7] H. Y. T. Ngan, G. K. H. Pang, S. P. Yung, and M. K. Ng, "Wavelet based methods on patterned fabric defect detection," Pattern Recognition, vol. 38, pp. 559-576, Apr 2005.
[8] W. J. Jasper, S. J. Garnier, and H. Potlapalli, "Texture characterization and defect detection using adaptive wavelets," Optical Engineering, vol. 35, pp. 3140-3149, Nov 1996.
[9] N. Sebe, M.S. Lew, "Wavelet based texture classification,"Pattern Recognition, Proceedings 15th International Conference on ,Vol. 3, Page(s):947 - 950 ,2000.
[10] S. Arivazhagan, L. Ganesan, V. Angayarkanni," Color texture classification using wavelet transform," Computational Intelligence and Multimedia Applications, Sixth International Conference on, 16-18 Aug. 2005 , Page(s): 315 - 320, 2005.
[11] L.Semler, , L. DettoriFurst, " Wavelet-based texture classification of tissues in computed tomography," Computer-Based Medical Systems, 2005. Proceedings. 18th IEEE Symposium, Page(s): 265 - 270, , 2005.
[12] T. Chang, C. J. kuo, "Texture analysis and classification with treestructured wavelet transform," IEEE trans. On Image proc., Vol.2, No.4, Page(s):429-441, Oct 1993.
[13] W. Y. Ma, B. S. Manjunath, " A comparison of wavelet transform features for texture image Annotation," IEEE Image Processing, 1995. Proceedings., International Conference on, Volume 2, Issue , 23-26 Oct 1995 Page(s):256 - 259 vol.2
[14] Rimac-Drlje, A. Keller, Z. Hocenski,," Neural Network Based Detection of Defects in Texture Surfaces," Proceedings of the IEEE International Symposium on Industrial Electronics, Vol. 3, Page(s): 1255 - 1260, June 2005.
[15] R. Schalkoff," Artificial Neural Networks," McGraw-Hill,1997.