Email this article A Novel Intelligent Technique for Recognition of Coke Optical Texture
Abstract
The classification and recognition of the coke optical texture is one of the key elements to determine the quality and guide the production of cokes. Since the traditional methods are not so ideal in the spatial and frequency domains, a novel algorithm is proposed in this paper to bridge the gap. Firstly, the coke micrograph is decomposed by a new contourlet packet (CP) for multi-scale and multi-direction, which introduces a nonsubsampled wavelet transform and a nonsubsampled directional filter banks (NSDFB). Furthermore, an adaptively weighted 2-directonal 2-dimension PCA method is put forward to extract the feature, which not only reduce the data dimensions, but also help to obtain a set of optimal basis in decomposed sub-bands. Finally, the classes of optical texture in coke micrograph are identified according to the knowledge-based similarity measure criteria on eigenvectors of selected basis. The experimental results indicate that the proposed scheme has a higher and more stable recognition rate than the conventional methods.
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References
H.Marsh, M.M. Escandell, F.R. Reinoso, Carbon, vol.37, pp.363-390, 1999.
http://dx.doi.org/10.1016/S0008-6223(98)00205-X
Q.L Lin, T.H Li, C.Z Zheng, Y Zhao, S.H Song, “Carbonization behavior of coal-tar pitch modified with divinylbenzene and optical texture of resultant semi-cokes”, J. Journal of Analytical and Applied Pyrolysis, vol.71, No.2, pp 817-826, 2004.
http://dx.doi.org/10.1016/j.jaap.2003.10.009
A.H. Reed, R.B. Pandey, and D.L. Lavoie, “Fractal Dimensionality of Pore and Grain Volume of a Siliciclastic Marine Sand”, International Journal of Modern Physics, vol.11, No.8, pp.1555-1557, 2000.
http://dx.doi.org/10.1142/S0129183100001358
D.L. Zhang, “Study on Determination of Coke Pore Structure by Image Analysis”, J. Fuel & Chemical Processes, vol.34, No.4, pp.175-178. 2003.
P.Z. Wang, X.Q. Mao, X.F. Mao, F. Zhou, “Coke Photomicrograph Segmentation Based on an Improved Mean Shift Method”, 2009 Asia-Pacific Conference on Information Processing, Shenzhen, China, July, 2009.
http://dx.doi.org/10.1109/APCIP.2009.143
M.N. Do, M. Vetterli, “The finite ridgelet transform for image representation”, J. IEEE Transactions on Image Processing vol. 2, No.1, pp.16–28. 2003.
http://dx.doi.org/10.1109/TIP.2002.806252
PMid:18237876
J.L. Starck, E. Candès, D.L. Donoho, “The curvelet transform for image denoising”, J. IEEE Transactions on Image Processing. vol. 11, No.6, pp. 670-684. 2002.
http://dx.doi.org/10.1109/TIP.2002.1014998
PMid:18244665
M.N. Do, M. Vetterli, The contourlet transform: an efficient directional multi-resolution image representation, IEEE Transactions on Image Processing, vol. 14, No. 12, pp. 2091-2106. 2005.
http://dx.doi.org/10.1109/TIP.2005.859376
PMid:16370462
R. Eslami, H. Radha, “Wavelet-based contourlet packet image coding”, in the Conference on Information Sciences and Systems, The Johns Hopkins University, March 2005.
S.Y Yang, M. Wang, L.C Jiao, “Radar target recognition using contourlet packet transform and neural network approach”, J. Signal Processing, vol.89, NO.4. pp. 394-409, 2009.
http://dx.doi.org/10.1016/j.sigpro.2008.09.015
J.L. Starck, J. Fadili, and F. Murtagh, “The undecimated wavelet decomposition and its reconstruction”, J. IEEE Transactions on Image Processing, vol.16, No.2 pp. 297-309, 2007.
http://dx.doi.org/10.1109/TIP.2006.887733
PMid:17269625
A.L. Cunha, J.P. Zhou, and M.N. Do, “The nonsubsampled contourlet transform: theory, design, and applications”, J. IEEE Transactions on Image Processing, vol.15, No.10 pp. 3089-3101, 2006.
http://dx.doi.org/10.1109/TIP.2006.877507
PMid:17022272
I.T. Jolliffe, Principal Component Analysis, Springer-Verlag, New York, 1986.
J. Yang, D. Zhang, A.F. Frangi, and J.Y. Yang, “Two-dimensional PCA: a new approach to appearance-based face representation and recognition,” J. IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 26, no. 1, pp. 131-137, Jan. 2004.
http://dx.doi.org/10.1109/TPAMI.2004.1261097
PMid:15382693
D.Q. Zhang, S.C. Chen, and J. Liu, “Representing image matrices: Eigenimages vs. Eigenvectors”, In the Proceedings of the 2nd International Symposium on Neural Networks (ISNN'05), Chongqing, China, vol. 2, pp. 659-664, 2005.
T.Y, J.H Ahn, Y.G Kim, Y.J Song, U.D Chang, D.W Kim, “Face recognition using a fusion method based on bidirectional 2DPCA”, J. Applied Mathematics and Computation, vol.20, No.5, pp.601–607, 2008.
P.J. Burt, E.H. Adelson, “The Laplacian pyramid as a compact image code”, J. IEEE Transactions on Communication, vol.31, No.4, pp.532~540. 1983.
http://dx.doi.org/10.1109/TCOM.1983.1095851
Y.M Lu, M.N. Do, “Multidimensional Directional Filter Banks and Surfacelets”, J. IEEE Transaction on image processing, Vol. 16, N0. 4, pp. 918-931. 2007.
F. G. Mayer, A. Z. Averbuch, and J.-O. Stromberg, “Fast adaptive wavelet packet image compression,” J.IEEE Trans. Image Processing, vol. 9, no. 5, pp. 792-800, May 2000.
http://dx.doi.org/10.1109/83.841526
PMid:18255451
M.N. Do and M. Vetterli, “Contourlets,” in Beyond Wavelets, Academic Press, New York, 2003.
H.L. X, T.X. Zhang, and Y.S. Moon, “A Translation- and Scale-Invariant Adaptive Wavelet Transform”, J. IEEE Trans. Image Processing, vol. 9, no. 12, pp. 2100-2108, 2000.
http://dx.doi.org/10.1109/83.887977
PMid:18262947
J.D. Wu, C.H Liu, “An expert system for fault diagnosis in internal combustion engines using wavelet packet transform and neural network”, J. Expert Systems with Applications, vol. 36, No.3, pp. 4278-4286. 2009.
http://dx.doi.org/10.1016/j.eswa.2008.03.008
F. S. Yang. Project Analysis and Application of Wavelet Transform. Beijing, Scientific Press, pp. 121-126, 1999.
A. Chao, R.L. Chazdon, R.K. Colwell and T.J. Shen, “A new statistical approach for assessing similarity of species composition with incidence and abundance data”, J. Ecology Letters, Vol. 8, pp.148–159. 2005.
http://dx.doi.org/10.1111/j.1461-0248.2004.00707.x
T. Ahonen, A. Hadid, M. Pietikainen, “Face recognition with local binary patterns”. In the proceedings of the 8th European Conference on Computer vision (ECCV), Prague, Czech Republic, pp.469~481. May 2004.
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