Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31103
Texture Characterization Based on a Chandrasekhar Fast Adaptive Filter

Authors: Mounir Sayadi, Farhat Fnaiech


In the framework of adaptive parametric modelling of images, we propose in this paper a new technique based on the Chandrasekhar fast adaptive filter for texture characterization. An Auto-Regressive (AR) linear model of texture is obtained by scanning the image row by row and modelling this data with an adaptive Chandrasekhar linear filter. The characterization efficiency of the obtained model is compared with the model adapted with the Least Mean Square (LMS) 2-D adaptive algorithm and with the cooccurrence method features. The comparison criteria is based on the computation of a characterization degree using the ratio of "betweenclass" variances with respect to "within-class" variances of the estimated coefficients. Extensive experiments show that the coefficients estimated by the use of Chandrasekhar adaptive filter give better results in texture discrimination than those estimated by other algorithms, even in a noisy context.

Keywords: Adaptive filters, Texture Analysis, statistical features, Chandrasekhar algorithm

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1244


[1] M. Acharyya and M. K. Kundu, "An adaptive approach to unsupervised texture segmentation using M-Band wavelet transform," Signal Processing, vol. 81, pp.1337-1356, 2001.
[2] P. Brodatz, Textures: a photographics album for artist and designers, Dovers, New York, 1966.
[3] G. Demoment and R. Reynaud, "Fast RLS adaptive algorithms and Chandrasekhar equations," Proceedings SPIE Adaptive Signal Processing, S. Haykin ed., vol P-1565, pp. 357-367, July 1991.
[4] X. C. Du, A. Brella and R. Longchamp, "Fast algorithm of Chandrasekhar type for ARMA model identification," Automatica, vol. 28, no. 5, pp. 927-934, 1992.
[5] M. Hadhoud, D W. Thomas. "The Two-dimensional adaptive LMS (TDLMS) algorithm" IEEE Transactions on Circuits and Systems, vol. 35, no. 5, pp 485-493, May 1988.
[6] T. Kasparis, D. Charalampidis, M. Georgiopoulos and J. Rolland, "Segmentation of texture images based on fractals and image filtering," Pattern Recognition, vol. 34, pp. 1963-1973, 2001.
[7] X. Liu and M. Najim, "A two dimensional fast lattice recursive least squares algorithm", IEEE Transactions on Signal Processing, vol. 44, no. 10, October 1996.
[8] C. S. Lu, P. C. Chung and C. F. Chen, "Unsupervised texture segmentation via wavelet transform," Pattern Recognition, vol. 30, no. 5, pp. 729-742, 1997.
[9] M. Morf, G. S. Sidhu and T. Kailath, "Some new algorithms for recursive estimation in constant, linear systems," IEEE Transactions on Automatic Control, vol. 19, pp 315-323, August 1974.
[10] T. Ojala, K. Valkealahti, E. Oja, M. Pietikainen. "Texture Discrimination with multi-dimensional distributions of signed gray-level differences," Pattern Recognition, vol. 34, no. 3, pp. 727-739, 2001.
[11] M. Sayadi, F. Fnaiech and M. Najim, "Multichannel linear and quadratic adaptive filtering based on the Chandrasekhar fast algorithm," IEEE Transactions on Signal Processing, vol. 47, pp. 860-864, March 1999.
[12] M. Sayadi and M. Najim, "Comparison of second and third order statistics based adaptive filters for texture characterization," Proceedings of ICASSP'99, Phoenix, Arizona, USA, pp. 3281-3284, March 1999.
[13] M. Sayadi, V. Buzenac and M. Najim, "Texture characterization using 2-D cumulant-based lattice adaptive filtering," Proceedings of ICASSP'98, Seattle, Washington, USA, pp. 2725-2728, 12-15 May 1998.
[14] F. Van Heijder, Image based measurement system, John Wiley and sons Edition, UK, 1995.
[15] G. Van de Wouwer, P. Scheunders, and D. V. Dyck, "Statistical Texture Characterization from Discrete Wavelet Representations," IEEE Transactions on Image Processing, vol. 8, pp. 592-598, Aug. 1999.