Abstract
In image processing problems density models are often used to characterise the local image statistics. In this paper a layered network structure is proposed, which consists of a large number of overlapping mixture distributions. This type of network is called a partitioned mixture distribution (PMD), and it may be used to apply mixture distribution models simultaneously to many different patches of an image.
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References
S. P. Luttrell, “The partitioned mixture distribution: an adaptive Bayesian network for low-level image processing”, IEE Proceedings on Vision, Image and Signal Processing, Vol. 141, No. 4,pp: 251 – 260, 1994.
D. M. Titterington, A. F. M. Smith and U. E. Makov, “Statistical analysis of finite mixture distributions”, Wiley, 1985.
A. P. Dempster, N. M. Laird and D. B. Rubin, “Maximum likelihood from incomplete data via the EM algorithm”, Journal of the Royal Statistical Society Series B, Vol. 39,pp: 1 – 37, 1977.
S. P. Luttrell, “An adaptive Bayesian network for low-level image processing”, Proceedings of the 3rd IEE International Conference on Artificial Neural Networks, Brighton,pp: 61 – 65, 1993.
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© 1994 British Crown Copyright
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Luttrell, S.P. (1994). The Partitioned Mixture Distribution: Multiple Overlapping Density Models. In: Skilling, J., Sibisi, S. (eds) Maximum Entropy and Bayesian Methods. Fundamental Theories of Physics, vol 70. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-0107-0_30
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DOI: https://doi.org/10.1007/978-94-009-0107-0_30
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-010-6534-4
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