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Probabilistic mixture-based image modelling

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    0360244 - ÚTIA 2012 RIV CZ eng J - Journal Article
    Haindl, Michal - Havlíček, Vojtěch - Grim, Jiří
    Probabilistic mixture-based image modelling.
    Kybernetika. Roč. 47, č. 3 (2011), s. 482-500. ISSN 0023-5954
    R&D Projects: GA MŠk 1M0572; GA ČR GA102/08/0593
    Grant - others:CESNET(CZ) 387/2010; GA MŠk(CZ) 2C06019; GA ČR(CZ) GA103/11/0335
    Institutional research plan: CEZ:AV0Z10750506
    Keywords : BTF texture modelling * discrete distribution mixtures * Bernoulli mixture * Gaussian mixture * multi-spectral texture modelling
    Subject RIV: BD - Theory of Information
    Impact factor: 0.454, year: 2011
    http://library.utia.cas.cz/separaty/2011/RO/haindl-0360244.pdf

    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.
    Permanent Link: http://hdl.handle.net/11104/0197840
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    0360244.pdf15.4 MBPublisher’s postprintopen-access
     
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