Texture Characterization Based on a Chandrasekhar Fast Adaptive Filter
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.
[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.
[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.
@article{"International Journal of Electrical, Electronic and Communication Sciences:61744", author = "Mounir Sayadi and Farhat Fnaiech", title = "Texture Characterization Based on a Chandrasekhar Fast Adaptive Filter", abstract = "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 = "Texture analysis, statistical features, adaptive filters, Chandrasekhar algorithm.", volume = "4", number = "3", pages = "616-5", }
