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Proceedings Paper

Nonlinear stochastic image modeling by means of multidimensional finite mixture distributions
Author(s): Benoit Maison; Luc Vandendorpe
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Paper Abstract

There is no formation model for natural images, unlike for speech or the specific signals generated by medical or satellite imagery. Autocorrelations and spectral analysis are convenient but limited tools. As Gaussiannity is nothing more than a rough approximation, higher order, or non-linear, models are required to account for the finer characteristics of real-world images. A joint modeling of neighboring pixels by means of finite mixture distributions is proposed. Each vector of M pixels is considered as being drawn form one of K M-variate distributions. Each component random vector is defined as the unitary transformation of a vector of M independent generalized-Gaussian random variables. This modeling technique permits to tackle a problem of high dimensionality (the estimation of a joint distribution of large order) with a limited number of parameters. The standard Expectation-Maximization (EM) or Stochastic EM algorithms can be used in order to estimate the model parameters from the data. The procedure can be applied to blocks of pixels or sets of subband samples and is tested on a variety of digital images. The applications range from image compression and joint source and channel coding to image restoration and image segmentation.

Paper Details

Date Published: 6 April 1998
PDF: 12 pages
Proc. SPIE 3304, Nonlinear Image Processing IX, (6 April 1998); doi: 10.1117/12.304619
Show Author Affiliations
Benoit Maison, Univ. Catholique de Louvain (Belgium)
Luc Vandendorpe, Univ. Catholique de Louvain (Belgium)


Published in SPIE Proceedings Vol. 3304:
Nonlinear Image Processing IX
Edward R. Dougherty; Jaakko T. Astola, Editor(s)

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