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Statements

Subject Item
n2:RIV%2F67985556%3A_____%2F10%3A00343253%21RIV11-MSM-67985556
rdf:type
n9:Vysledek skos:Concept
dcterms:description
A novel generative colour texture model based on multivariate Bernoulli mixtures is proposed. A measured multispectral texture is spectrally factorised and multivariate Bernoulli mixtures are further learned from single bit planes of the orthogonal monospectral components and used to synthesise and enlarge these monospectral binary factor components. Texture synthesis is based on easy computation of arbitrary conditional distributions from the model. Finally single synthesised monospectral texture bit planes are transformed into the required synthetic multispectral texture. This model can easily serve not only for texture enlargement but also for segmentation, restoration, and retrieval or to model single factors in complex Bidirectional Texture Function (BTF) space models. The strengths and weaknesses of the presented Bernoulli mixture based approach are demonstrated on several colour texture examples. A novel generative colour texture model based on multivariate Bernoulli mixtures is proposed. A measured multispectral texture is spectrally factorised and multivariate Bernoulli mixtures are further learned from single bit planes of the orthogonal monospectral components and used to synthesise and enlarge these monospectral binary factor components. Texture synthesis is based on easy computation of arbitrary conditional distributions from the model. Finally single synthesised monospectral texture bit planes are transformed into the required synthetic multispectral texture. This model can easily serve not only for texture enlargement but also for segmentation, restoration, and retrieval or to model single factors in complex Bidirectional Texture Function (BTF) space models. The strengths and weaknesses of the presented Bernoulli mixture based approach are demonstrated on several colour texture examples.
dcterms:title
Colour Texture Representation Based on Multivariate Bernoulli Mixtures Colour Texture Representation Based on Multivariate Bernoulli Mixtures
skos:prefLabel
Colour Texture Representation Based on Multivariate Bernoulli Mixtures Colour Texture Representation Based on Multivariate Bernoulli Mixtures
skos:notation
RIV/67985556:_____/10:00343253!RIV11-MSM-67985556
n3:aktivita
n7:Z n7:P
n3:aktivity
P(1M0572), P(2C06019), P(GA102/08/0593), Z(AV0Z10750506)
n3:dodaniDat
n8:2011
n3:domaciTvurceVysledku
n5:9690875 n5:2890542 n5:5728525
n3:druhVysledku
n19:D
n3:duvernostUdaju
n4:S
n3:entitaPredkladatele
n18:predkladatel
n3:idSjednocenehoVysledku
251185
n3:idVysledku
RIV/67985556:_____/10:00343253
n3:jazykVysledku
n14:eng
n3:klicovaSlova
Texture modeling; Bernoulli mixture; EM algorithm
n3:klicoveSlovo
n16:Texture%20modeling n16:Bernoulli%20mixture n16:EM%20algorithm
n3:kontrolniKodProRIV
[226F849B4077]
n3:mistoKonaniAkce
Kuala Lumpur
n3:mistoVydani
Los Alamitos
n3:nazevZdroje
10th International Conference on Information Sciences, Signal Processing and their Applications
n3:obor
n11:BD
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
3
n3:projekt
n15:1M0572 n15:GA102%2F08%2F0593 n15:2C06019
n3:rokUplatneniVysledku
n8:2010
n3:tvurceVysledku
Haindl, Michal Grim, Jiří Havlíček, Vojtěch
n3:typAkce
n20:WRD
n3:zahajeniAkce
2010-05-10+02:00
n3:zamer
n13:AV0Z10750506
s:numberOfPages
4
n21:hasPublisher
IEEE
n6:isbn
978-1-4244-7166-9