Institute of Information Theory and Automation

Publication details

Probabilistic mixture-based image modelling

Journal Article

Haindl Michal, Havlíček Vojtěch, Grim Jiří

serial: Kybernetika vol.47, 3 (2011), p. 482-500

research: CEZ:AV0Z10750506

project(s): 1M0572, GA MŠk, 387/2010, CESNET, 2C06019, GA MŠk, GA102/08/0593, GA ČR, GA103/11/0335, GA ČR

keywords: BTF texture modelling, discrete distribution mixtures, Bernoulli mixture, Gaussian mixture, multi-spectral texture modelling

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abstract (eng):

During the last decade we have introduced probabilistic mixture models into image modelling area, which present highly atypical and extremely demanding applications for these models. This difficulty arises from the necessity to model tens thousands correlated data simultaneously and to reliably learn such unusually complex mixture models. Presented paper surveys these novel generative colour image models based on multivariate discrete, Gaussian or Bernoulli mixtures, respectively and demonstrates their major advantages and drawbacks on texture modelling applications. Our mixture models are restricted to represent two-dimensional visual information. Thus a measured 3D multispectral texture is spectrally factorized and corresponding multivariate mixture models are further learned from single orthogonal mono-spectral components and used to synthesise and enlarge these mono-spectral factor components.


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Last modification: 21.12.2012
Institute of Information Theory and Automation