Brodatz, P.: A Photographic Album for Artists and Designers. Dover, New York, 1996.
Bishop, C. M.: Novelty detection and neural network validation. IEE Proceedings: Vision, Image and Signal Processing
141(6) (1994), 217–222.
Jain, A. K.: Fundamentals of Digital Image Processing. Prentice Hall, London, 1989.
Gonzalez, R. C. and Wintz, P.: Digital Image Processing, Addison-Wesley Publishing Company, 1993.
Tuceryan, M. and Jain, A. K.: Texture analysis, In: C. H. Chen, L. F. Pau, and P. S. P. Wang, (eds.), The Handbook of Pattern Recognition and Computer Vision, World Scientific Publishing Co., 1988, pp. 207–248.
Duda, R. O. and Hart, P. E.: Pattern Classification and Scene Analysis, John Wiley, New York, 1973.
Papoulis, A.: Probability, Random Variables, and Stochastic Processes, McGraw-Hill, New York-Toronto, 1991. Third edition.
Geman, S. and German, D.: Stochastic relaxation, Gibbs distributions and the bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence
6(6) (1984), 721–741.
Chellappa, R. and Jain, A.: Markov Random Fields: Theory and Applications, Academic Press, 1993.
Romberg, J. K., Choi, H. and Baraniuk, R. G.: Bayesian wavelet domain image modeling using Hidden Markov trees, In: International Conference on Image Processing-ICIP'99, Kobe, Japan, October 1999, pp. 1–5.
De Bonet, J. S. and Viola, P.: Texture recognition using a non-parametric multi-scale statistical model. In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition, 1988.
Wainwright, M. J., Simonceli, E. P. and Willsky, A. S.: Random cascades on wavelet trees and their use on analyzing and modeling natural images. Applied and Computational Harmonic Analysis
11(1) (2001), 89–123.
Mallat, S. G.: A Wavelet Tour of Signal Processing Academic Press, San Diego, 1998.
Simoncelli, E. P.: Statistical models for images: Compression, restoration and synthesis. In 31st Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, November 1997, pp. 2–5.
Portilla, J. and Simoncelli, E. P.: A parametric texture model based on joint statistics of complex wavelet coefficients. International Journal of Computer Vision 40, 49–71, December 2000.
Adelson, E. H., Simoncelli, E. P. and Hingorani, R.: Orthogonal pyramid transforms for image coding. In SPIE Visual Communications and Image Processing II, volume 845, October 1987, pp. 50–58.
Daugman, J. G.: Complete discrete 2D Gabor transforms by neural networks for image analysis and compression. IEEE Transactions on ASSP
36(7) (1988), 1169–1179.
Jain, A. K. and Farrokhnia, F.: Unsupervised texture segmentation using Gabor filters. Pattern Recognition
4(12) (1991), 1167–1186.
Nestares, O., Navarro, R., Portilla, J. and Tabernero, A.: Efficient spatial-domain implementation of a multiscale image representation based on Gabor functions. Journal of Electronic Imaging, SPIE
07(01) (1998), 166–173.
Marr, D.: Vision. Imprint FREEMAN, New York, 1982.
Jensen. F. V.: An Introduction to Bayesian Networks. Springer Verlag, New York, 1996.
Crouse, M. S., Nowak, R. D. and Baraniuk, R. G.: Wavelet-based signal processing using Hidden Markov Models. IEEE Transactions on Signal Processing,
46, 886–902, April 1998.
Jordan, M.: Learning in Graphical Models. The MIT Press, Cambridge, Massachusetts, London, England, 1999.
Rabiner, L. R. and Juang, B. H.: An Introduction to Hidden Markov Models. In IEEE ASSP Magazine, January 1986, pp. 4–16.
Dempster, A. P., Laird, N. M. and Rubin, D. B.: Maximum likelihood from incomplete data via the EM algorithm. Proceedings of the Royal Statistical Society
B-39 (1977), 1–38.
Frey, B. J.: Graphical Models for Machine Learning and Digotal Communiation. The MIT Press, Cambridge, Massachusetts London, England, 1998.
Opper, M. and Saad, D.: Advanced Mean Field Methods. The MIT Press, Cambridge, 2001.
Ronen, O., Rohlicek, J. R. and Ostendorf, M.: Parameter Estimation of Dependence Tree Models Using the EM Algorithm. IEEE Signal Processing Letters
2(8) (1995), 157–159.
Kschischang, F. R., Frey, B. J. and Loeliger, H. A.: Factor graphs and the Sum-Product Algorithm. IEEE Transactions on Information Theory
47(2) (2001), 498–519.
Efron B. and Tibshirani, R.: An Introduction to the Bootstrap. Chapman and Hall, New York, 1993.
Bellman, R. E.: Adaptive Control Processes. Princeton University Press, Princeton, NJ, 1961.
Geman, S., Bienenstock, E. and Doursat, R.: Neural networks and the bias-variance dilemma. Neural Computation
4 (1992), 1–58.
Friedman, N. and Russel, S.: Image segmentation in video sequences: A probabilistic approach. In The Thirteenth Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann Publishers, 1997, pp. 175–181.
Stauffer, C. and Grimson, W. E. L.: Adaptive background mixture model for real-time tracking. In IEEE Computer Society Conference om Computer Vision and Pattern Recognition, Cat. No. PR00149, volume 2, June 23-25 1999, pp. 22–46.
McLachlan, G. and Peel, D.: Finite Mixture Models. Wiley Series in Probability and Statistics, New York, 2000.
Bilmes, J A.: A gentle tutorial of the EM algorithm and its application to parameter estimation for Gaussian mixture and Hidden Markov models. Technical report, tr-97-021, International Computer Science Institute and Computer Science Division U. C. Berkeley, 1998.
Stainvas, I. and Lowe, D.: Towards sea surface pollution detection from visible band images. IEICE Transactions on Electronics a Special Issue on New Technologies in Signal Processing for Electromagnetic-wave Sensing and Imaging, E84C(12), 1848–1856, December 2001.
Stainvas, I. and Lowe, D.: A generative model for separating illumination and reflectance from images. Technical report, Aston University, NCRG/2002/024, June 2002.
Heskes, T.: Selecting weighing factors in logarithmic opinion pools. Advances in Neural Information Processing Systems, 1998.
Hinton, G. E.: Products of experts. In Proceedings of the Ninth International Conference on Artificial Neural Networks (ICANN99), volume 1, Edinburgh, Scotland, 1999, pp. 1–6.
Ghahramani, Z. and Jordan, M.: Factorial Hidden Markov models. Machine Learning, 29 (1997), 245–273.