Dynamic trees for image modelling

Image and Vision Computing

Volumn 21, Issue 10, 2003, Pages 865-877

  Adams, Nicholas J ^a   Williams, Christopher K I ^a  

^a UNIVERSITY OF EDINBURGH   (United Kingdom)

Author keywords

Bayesian image modelling; Belief networks; Dynamic tree; Expectation maximisation; Mean field; Variational inference

Indexed keywords

APPROXIMATION THEORY; IMAGE SEGMENTATION; MATHEMATICAL MODELS; PROBABILITY; TREES (MATHEMATICS);

DYNAMIC TREES;

IMAGE ANALYSIS;

EID: 0042009578     PISSN: 02628856     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0262-8856(03)00073-8     Document Type: Conference Paper

References (34)

1. N.J. Adams, Dynamic Trees: A Hierarchical Probabilistic Approach to Image Modelling. PhD Thesis, Institute for Adaptive and Neural Computation, Artificial Intelligence, Division of Informatics, University of Edinburgh, 5 Forrest Hill, Edinburgh, EH1 2QL, UK, 2001.
2. Adams N.J., Storkey A.J., Ghahramani Z., Williams C.K.I. MFDTs: Mean Field Dynamic Trees. Sanfeliu A., Villanueva J.J., Vanrell A., Alquézar R., Huang T., Serra J. Proceedings of 15th International Conference Pattern Recognition, Image speech and Signal Processing. vol. 3:2000;151-154 IEEE Computer Society.
3. Adams N.J., Williams C.K.I. SDTs: Sparse Dynamic Trees. In Proceedings of Ninth International Conference on Artificial Neural Networks. 1999;. pp. 527-532.
4. Adams N.J., Williams C.K.I., Storkey A.J. Comparing Mean Field and Exact EM in Tree Structured Belief Networks. In Fourth International ICSC Symposium on Soft Computing and Intelligent Systems for Industry. 2001;ICSC-NAISO Academic.
5. Bouman C.A., Shapiro M. A multliscale random field model for bayesian image segmentation. IEEE Transactions on Image Processing. 3:(2):1994;162-177.
6. Charniak E. Statistical Language Learning. 1993;MIT Press, Cambridge, MA.
7. Chellappa R., Chatterjee S. Classification of textures using Guassian Markov random fields. IEEE Transactions on Acoustics, Speech and Signal Processing. 33:1985;959-963.
8. Chou P.A. Recognition of equations using a two-dimentional stochastic context-free grammar. Visual Communications and Image Processing IV. 1199:1989;852-863.
9. Crouse M., Nowak R., Baraniuk R. Wavelet-based statistical signal processing using hidden Markov models. IEEE Transactions on Signal Processing. 46:1998;886-902.
10. Dayan P., Hinton C.E., Neal R.M., Zemel R.S. The Helmholtz Machine. Neural Computation. 7:(5):1995.
11. De Bonet J.S., Viola P.A. A non-parametric multi-scale statistical model for natural images. Jordan M.I., Kearns M.J., Solla S.A. Advances in Neural Information Processing Systems 10. 1998;MIT Press, Cambridge, MA.
12. Feng X., Williams C.K.I. Training Bayesian networks for image segmentation. In Proceedings of SPIE. 3457:(July):1998.
13. X. Feng, C.K.I. Williams, S.N. Felderhof. Combining belief networks and neural networks for scene segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(4) (2002) 467-483.
14. Fieguth P.W., Willsky A.S., Karl W.C. Multiresolution stochastic imaging of satellite oceanographic altimetric data. IEEE International Conference on Image Processing. 2:1994;1-5.
15. Friedman N. The Bayesian structural em algorithm, In Proceedings of the Fourteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-98). 1998;Morgan Kaufman, San Francisco, CA. pp. 129-138.
16. Geiger D., Heckerman D. Knowledge representation and inference in similarity networks and Bayesian multinets. Artificial Intelligence. 82:1996;45-74.
17. Geman S., Geman D. Stochastic relaxation; Gibbs distributions, and the Bayesian restoration of images. In IEEE Transactions on Pattern Analysis and Machine Intelligence. 6:(6):1984;721-741.
18. S. Geman, K. Manbeck, Experiments in syntactic recognition, Technical Report CICS-P-411, Division of Applied Mathematics, Brown University, Providence, RI 02912 USA, 1994.
19. Hinton G.E., Sallans B., Ghahramani Z., Hierarchical A. A Hierarchical community of experts. Bishop C.M. Neural Networks and Machine Learning. 1998;Springer, New York.
20. Hinton G.E., Ghahramani Z., Teh Y.W. Learning to parse images. Solla S.A., Leen T.K., Müller K.R. Advances in Neural Information Processing Systems 12. 2000;463-493 MIT Press, Cambridge, MA.
21. Irving W.W., Fieguth P.W., Wilsky A.S. An overlapping tree approach to multiscale stochastic modeling and estimation. IEEE Transactions on Image Processing. 6:(11):1997;1517-1529.
22. Jordan M.I., Ghahramani Z., Jaakkola T.S., Saul L.K. An Introduction to variational methods for graphical models. Jordan M.I. Learning in Graphical Models. 1998;105-161 Kluwer, Dordecht.
23. Lauritzen S.L. Graphical Models. 1996;Oxford University Press, Oxford.
24. Little R.J.A., Rubin D.B. Statistical Analysis with Missing Data. 1987;Wiley, New York.
25. Luettgen M.R., Willsky A.S. Likelihood calculation for a class of multiscale stochastic models, with application to texture discrimination. IEEE Transactions on Image Processing. 4:(2):1995;194-207.
26. Montanvert A., Meer P., Rosenfeld A. Hierarchical image analysis using irregular tessellations. IEEE Transactions Pattern Analysis and Machine Intelligence. 13:(4):1991;307-316.
27. Pearl J. Probabilistic Reasoning in Intelligent Systems. second ed: 1988;Morgan Kaufman, San Francisco, CA.
28. Saul L.K., Jordan M.I. Exploiting tractable substructures in intractable networks. Touretzky D.S., Mozer M.C., Hasselmo M.E. Exploiting Tractable Substructures in Intractable Networks 8. 1996;MIT Press, Cambridge, MA.
29. Storkey A.J., Williams C.K.I. Dynamic positional trees for structural image analysis, In Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics. 2001;Morgan Kaufmann, Los Altos, CA.
30. von der Malsburg C. The correlation theory of brain function, Internal Report 81-2, Max-Planck-Institut für Biophysikalische Chemie, 1981. Reprinted in Schulten K., van Hemmen H.-J. Models of Neural Networks. second ed., 1994;Springer, New York.
31. von der Malsburg C. The correlation theory of brain function, Internal Report 81-2, Max-Planck-Institut für Biophysikalische Chemie, 1981. Reprinted in Schulten K., van Hemmen H.-J. Models of Neural Networks. second ed., 1994;Springer, New York.
32. von der Malsburg C. Arbib M.A. Handbook of Brain Theory and Neural Networks. 1995;329-331 MIT Press, Cambridge, MA.
33. C.K.I. Williams. Using deterministic Boltzmann machines for discriminating temporally distorted strings, MSc Thesis, Department of Computer Science, University of Toronto, 1990.
34. Williams C.K.I., Adams N.J. DTs: Dynamic Trees. Kearns M.J., Solla S.A., Cohn D.A. Advances in Neural Information Processing Systems 11. 1999;634-640 MIT Press, Cambridge, MA.