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Probabilistic mixture-based image modelling
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SYSNO ASEP 0360244 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Probabilistic mixture-based image modelling Author(s) Haindl, Michal (UTIA-B) RID, ORCID
Havlíček, Vojtěch (UTIA-B) RID
Grim, Jiří (UTIA-B) RID, ORCIDSource Title Kybernetika. - : Ústav teorie informace a automatizace AV ČR, v. v. i. - ISSN 0023-5954
Roč. 47, č. 3 (2011), s. 482-500Number of pages 19 s. Language eng - English Country CZ - Czech Republic Keywords BTF texture modelling ; discrete distribution mixtures ; Bernoulli mixture ; Gaussian mixture ; multi-spectral texture modelling Subject RIV BD - Theory of Information R&D Projects 1M0572 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) GA102/08/0593 GA ČR - Czech Science Foundation (CSF) CEZ AV0Z10750506 - UTIA-B (2005-2011) UT WOS 000293207900011 EID SCOPUS 83455221186 Annotation 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. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2012
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