Počet záznamů: 1
Probabilistic mixture-based image modelling
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SYSNO ASEP 0360244 Druh ASEP J - Článek v odborném periodiku Zařazení RIV J - Článek v odborném periodiku Poddruh J Článek ve WOS Název Probabilistic mixture-based image modelling Tvůrce(i) Haindl, Michal (UTIA-B) RID, ORCID
Havlíček, Vojtěch (UTIA-B) RID
Grim, Jiří (UTIA-B) RID, ORCIDZdroj.dok. Kybernetika. - : Ústav teorie informace a automatizace AV ČR, v. v. i. - ISSN 0023-5954
Roč. 47, č. 3 (2011), s. 482-500Poč.str. 19 s. Jazyk dok. eng - angličtina Země vyd. CZ - Česká republika Klíč. slova BTF texture modelling ; discrete distribution mixtures ; Bernoulli mixture ; Gaussian mixture ; multi-spectral texture modelling Vědní obor RIV BD - Teorie informace CEP 1M0572 GA MŠMT - Ministerstvo školství, mládeže a tělovýchovy GA102/08/0593 GA ČR - Grantová agentura ČR CEZ AV0Z10750506 - UTIA-B (2005-2011) UT WOS 000293207900011 EID SCOPUS 83455221186 Anotace 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. Pracoviště Ústav teorie informace a automatizace Kontakt Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Rok sběru 2012
Počet záznamů: 1