Segmentation of Steel Surfaces towards Defect Detection Using New Gabor Composition Method
Subject Areas : electrical and computer engineeringS. J. Alemasoom 1 , A. Monadjemi 2 , H. A. Alemasoom 3
1 -
2 -
3 -
Keywords: Clustering defect detection Gabor composition Gabor filters K means classifier texture segmentation,
Abstract :
The images of steel surfaces are generally textural images. There are different texture analysis methods to extract features from these images. In those methods using multi-scale/multi-directional analysis, Gabor filters are used for feature extraction. In this paper, we extract texture features using the optimum Gabor filter bank. This filter bank is designed in a way that diverse filtering frequency and orientation will allow it to extract considerable amounts of texture information from the input images. We also introduce a new method called Gabor composition for segmentation and defect detection of steel surfaces. In this method, using two different algorithms, the input image is decomposed into detail images using an appropriate Gabor filter bank and then selected detail images are re composed. The created feature map illustrates the defective areas well. By calculating data distribution of detail images and comparing them, the second method of Gabor composition can accomplish segmentation without needing the normal images and the number of detail images to re-compose. Furthermore, we did different tests towards optimizing of segmentation by means of classifiers. Using a K-means classifier and adding gray levels to the extracted features, complete the segmentation procedure. The experimental results show that the Gabor composition method in most of the tests has got better defect detection performance than the ordinary K-means classifier and the standard wavelet method; also the Second method of Gabor composition has got the best performance over all.
[1] J. Alemasoom, Texture Segmentation towards Defect Detection, M. Sc. Thesis, University of Isfahan, Oct. 2008.
[2] J. P. Yun, S. Choi, J. W. Kim, and S. W. Kim, "Automatic detection of cracks in raw steel block using Gabor filter optimized by univariate dynamic encoding algorithm for searches (uDEAS)," NDT & E Int., vol. 42, no. 5, pp. 389-397, Jul. 2009.
[3] H. Sari-Sarraf and J. Goddard, "Vision system for on-loom fabric inspection," IEEE Trans. on Industry Applications, vol. 35, no. 8, pp. 1252-1259, Nov./Dec. 1999.
[4] H. Sari-Sarraf, J. S. Goddard, B. R. Abidi, and M. A. Hunt, "Vision system for on-line characterization of paper slurry," Int. J. of Imaging Systems and Technology, vol. 11, no. 4, pp. 231-242, 2000.
[5] A. Kumar and G. K. H. Pang, "Defect detection in textured materials using Gabor filters," IEEE Trans. on Industry Applications, vol. 38, no. 2, pp. 425-440, Mar./Apr. 2002.
[6] M. Petrou, J. Kitter, and K. Y. Song, "Automatic surface crack detection on textured materials," J. of Materials Processing Technology, vol. 56, no. 1-4, pp. 158-167, Jan. 1996.
[7] A. Monadjemi, Towards Efficient Texture Classification and Abnormality Detection, Ph. D Thesis, Bristol University, 2004.
[8] M. Mirmehdi and M. Petrou, "Segmentation of color textures," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 22, no. 2, pp. 142-159, Feb. 2000.
[9] X. Xie and M. Mirmehdi, "TEXEMS: texture exemplars for defect detection on random textured surfaces," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 29, no. 8, pp. 1454-1464, Aug. 2007.
[10] X. Liu and D. Wang, "Image segmentation using local spectral histograms," IEEE Trans. on Image Processing, vol. 15, no. 10, pp. 3066-3077, Oct. 2006.
[11] T. Weldon, W. Higgins, and D. Dunn, "Gabor filter design for multiple texture segmentation," Optical Engineering, vol. 35, no. 10, pp. 2852-2863, 1996.
[12] T. Ojala, M. Pietikainen, and D. Harwood, "A comparative study of texture measures with classification based on feature distribution," IEEE Trans. on Pattern Recognition, vol. 29, no. 1, pp. 51-59, Jan. 1996.
[13] R. Haralick, K. Shanmugam, and I. Dinstein, "Textural features for image classification," IEEE Trans. on Systems, Man, and Cybernetics, vol. 3, no. 6, pp. 610-621, Nov. 1973.
[14] D. Clausi and M. Jernigan, "Designing Gabor filters for optimal texture separability," Pattern Recognition, vol. 33, no. 11, pp. 1835-1849, Nov. 2000.
[15] A. K. Jain and F. Farrokhnia, "Unsupervised texture segmentation using Gabor filters," Pattern Recognition, vol. 24, no. 12, pp. 1167-1186, 1991.
[16] E. Abouei Mehrizi, A. Monadjemi, and M. Ashorian, "Color steel plates defect detection using wavelet and color analysis," Int. J. of Computer Science & Information Security, IJCSIS, vol. 8, no. 2, pp. 285-292, May 2010.