Počet záznamů: 1

Probabilistic mixture-based image modelling

  1. 1.
    0360244 - UTIA-B 2012 RIV CZ eng J - Článek v odborném periodiku
    Haindl, Michal - Havlíček, Vojtěch - Grim, Jiří
    Probabilistic mixture-based image modelling.
    Kybernetika. Roč. 47, č. 3 (2011), s. 482-500 ISSN 0023-5954
    Grant CEP: GA MŠk 1M0572; GA ČR GA102/08/0593
    Grant ostatní: CESNET(CZ) 387/2010; GA MŠk(CZ) 2C06019; GA ČR(CZ) GA103/11/0335
    Výzkumný záměr: CEZ:AV0Z10750506
    Klíčová slova: BTF texture modelling * discrete distribution mixtures * Bernoulli mixture * Gaussian mixture * multi-spectral texture modelling
    Kód oboru RIV: BD - Teorie informace
    Impakt faktor: 0.454, rok: 2011
    http://library.utia.cas.cz/separaty/2011/RO/haindl-0360244.pdf 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.
    Trvalý link: http://hdl.handle.net/11104/0197840
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