6 April 1998 Nonlinear stochastic image modeling by means of multidimensional finite mixture distributions
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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.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Benoit Maison, Benoit Maison, Luc Vandendorpe, Luc Vandendorpe, } "Nonlinear stochastic image modeling by means of multidimensional finite mixture distributions", Proc. SPIE 3304, Nonlinear Image Processing IX, (6 April 1998); doi: 10.1117/12.304619; https://doi.org/10.1117/12.304619
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