IJSER Home >> Journal >> IJSER
International Journal of Scientific and Engineering Research
ISSN Online 2229-5518
ISSN Print: 2229-5518 10    
Website: http://www.ijser.org
scirp IJSER >> Volume 2, Issue 10, October 2011 Edition
Novel Defect Segmentation Technique in Random Textured Tiles
Full Text(PDF, 3000)  PP.  
Author(s)
Aborisade, D.O and Ojo, J. A
KEYWORDS
Detection of defect, Gabor filters, Self-Organizing Map, Canny operator.
ABSTRACT
In this paper problem of detecting different type of defects on random textured tiles surfaces is addressed. Since Gabor filters allows optimal localization both in the spatial domain and in the spatial-frequency domain it is been utilized in the proposed technique to extract texture features which are useful for detecting defect edges on the tile. Kohonen's Self-Organizing Maps (SOM) is used for reducing the feature vectors to obtain 1-dimensional feature map (scalar image). The output of the SOM is smoothed with Gaussian filtering mask and Canny's edge edge-detection method is applied to the smoothed feature map image to obtain the edge map of the detected defect from the tile surface. The results obtained when the proposed technique is tested on various random texture tiles confirm its efficiency.
References
[1] Vasilic, S., Z. Hocenski, 2006. “The Edge Detecting Methods in Ceramic Tiles Defects Detection,” IEEE ISIE, pp: 469-472.

[2] Williams, D., G.B. Finney, J.B. Gomm and J.T. Atkinson, 1994. “Inspection of ceramic table-ware for quality control using a neural network vision system,” SPIE Mach. Vis. Appl. Ind. Insp. II, vol. SPIE 2183, 145-154.

[3] Hocenski, Z., S. Vasilic and V. Hocenski, 2006. ""Improved Canny Edge Detector in Ceramic Tiles Defect Detection"", IEEE 32nd Annual Conference on Industrial Electronics, IECON, pp: 3328-3331.

[4] Xie, X. and M. Mirmehdi, 2007. “TEXEMS: Texture Exemplars for Defect Detection on Random Textured Surfaces,” IEEE Trans. On PAMI., Vol. 29, No. 8, August, pp: 1454-1464.

[5] Novak, I., Z. Hocenski, 2005. ""Texture Feature Extraction for a Visual Inspection of Ceramic Tiles"", IEEE ISIE, June 20-23, 2005, Dubrovnik, Croatia, Trans. Pattern Analysis and Machine Intelligence, pp: 1279-1283.

[6] S. Arivazhagan, et al, “Fault segmentation in fabric images using Gabor wavelet transform,” Machine Vision and Applications (2006) 16(6): 356–363.

[7] Mallat, S. “Multi-resolution approximations and wavelet orthonormal bases of L2(R),” Transactions of American Mathematical Society 315, 69–87 (1989)

[8] Mallat, S. ”A theory for multi-resolution signal decomposition: The wavelet representation,” IEEE Transactions on Pattern Recognition and Machine Intelligence 11, 674–693 (1989)

[9] E. Salari and Z. Ling “Texture segmentation using hierarchical wavelet decomposition,” Pattern Recognition, 28:1819–1824, 1995.

[10] C. Lu, P. Chung, and C. Chen “Unsupervised texture segmentation via wavelet transform,” Pattern Recognition, 30:729–742, 1997.

[11] 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.

[12] S. Rimac-Drlje, A. Keller, K. E. Nyarko, “Self-Learning System for Surface Failure Detection,” http://wwwciteseerx. ist.psu.edu/viewdoc/download

[13] A. Ahmadyfard ,et al,” A Novel Approach for Detecting Defects of Random Textured Tiles Using Gabor Wavelet,” World Applied Sciences Journal 7 (9): 1114-1119, 2009

[14] A. Khodaparast and A. Mostafa. On line quality control of tiles using wavelet and statistical properties. In Proceedings of the 2nd Iranian Conference on Machine Vision and Image Processing, pages 153–159, February 2003.

[15] C. Boukouvalas, et al., “ASSIST: automatic system for surface inspection and sorting of tiles,” J. Materials Processing Technology, vol.82, pp. 179-188, 1998.

[16] Mahkameh S. Mostafavi, “A New Method in Detection of Ceramic Tiles Color Defects using Genetic C-Means Algorithm,” Proceeding of World Academy of Science, Engineering and Technology, vol. , pp: 168-171, DEC. 2006.

[17] D. Dunn and W. Higgins, “Optimal Gabor Filters for Texture Segmentation,” IEEE Trans. Image Proc., vol.4, no. 7, 1995, pp: 947-964.

[18] J.-C. Liu and G. Pok., “Texture edge detection by feature encoding and predictive model,” IEEE International Conference on Acoustics, Speech, and Signal Processing, volume 2, pages 1105–1108, March 1999.

[19] D. Gabor: 'Theory of communication', J. Inst. Elec. Eng., 1946, 93, 429-457.

[20] Daugman, J.G. “Two-dimensional spectral analysis of cortical receptive field profiles,” Vision Res. 20, 847–856 (1980)

[21] J. G. Daugman “'Uncertainty relation for resolution in space, spatial-frequency, and orientation optimized by twodimensional visual cortical filters,” J. Opt. Soc. Amer., 1985, 2, 1160-1169.

[22] Bodnarova, A., M. Bennamoun and S. J. Latham, 2000. “A constrained minimization approach to optimize Gabor filters for detecting flaws in woven textiles,” Acoustics, Speech and Signal Processing. 6(5-9): 3606-3609.

[23] Kumar, A. and G. Pang, 2002. “Defect Detection in Textured Materials Using Gabor Filters,” IEEE Trans. Industrial Applications, vol. 38, no. 2, pp: 425-440.

[24] Young, R.A. “The Gaussian derivative model for spatial vision,” Retinal mechanisms. Spatial visions 2, 273–293 (1987).

[25] T. Kohonen, “The self-organizing map”, Proc. IEEE, Vol. 78, pp. 1464-1480, 1990.

[26] T. Kohonen, Self-Organizing Maps (Third Ed.), Springer, 2001.

[27] T. Villmann, E. Merenyi, “Extensions and modifications of the Kohonen-SOM and applications in remote sensing image analysis”, in U. Seiffert and L. C. Jain (Eds.), Self Organizing Maps: Recent Advances and Applications, Springer-Verlag, Berlin, pp. 121-145, 2001.

[28] I. Reljin, B. Reljin, G. Jovanović, “Clustering and Mapping Spatial-Temporal Datasets Using SOM Neural Networks Journal of Automatic Control, University of Belgrade; vol. 13(1):55-60, 2003.

[29] Daugman, J.G.: Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression. IEEE Transactions on Acoustics, Speech, Signal Processing 36, 1169– 1179 (1988)

Untitled Page