Probabilistic mixture-based image modelling

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F11%3A00360244" target="_blank" >RIV/67985556:_____/11:00360244 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61384399:31160/11:00038711

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Probabilistic mixture-based image modelling

Popis výsledku v původním jazyce

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 theirmajor 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 mixturemodels are further learned from single orthogonal mono-spectral components and used to synthesise and enlarge these mono-spectral factor components.

Název v anglickém jazyce

Probabilistic mixture-based image modelling

Popis výsledku anglicky

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 theirmajor 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 mixturemodels are further learned from single orthogonal mono-spectral components and used to synthesise and enlarge these mono-spectral factor components.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BD - Teorie informace

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2011

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Kybernetika

ISSN

0023-5954

e-ISSN

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Svazek periodika

47

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

19

Strana od-do

482-500

Kód UT WoS článku

000293207900011

EID výsledku v databázi Scopus

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