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Statements

Subject Item
n2:RIV%2F67985556%3A_____%2F10%3A00343263%21RIV11-MSM-67985556
rdf:type
skos:Concept n16:Vysledek
dcterms:description
Content-based image retrieval systems (CBIR) typically query large image databases based on some automatically generated colour and textural features. Optimal robust features should be geometry and illumination invariant. Although image retrieval has been an active research area for many years this difficult problem is still far from being solved. We introduce fast and robust textural features that allow retrieving images with similar scenes comprising colour textured objects viewed with different illumination. The proposed textural features that are invariant to illumination spectrum and extremely robust to illumination direction. They require only a single training image per texture and no knowledge of illumination direction, brightness or spectrum. These feature utilises utilise illumination invariant features extracted from three different Markov random field (MRF) based texture representations. Content-based image retrieval systems (CBIR) typically query large image databases based on some automatically generated colour and textural features. Optimal robust features should be geometry and illumination invariant. Although image retrieval has been an active research area for many years this difficult problem is still far from being solved. We introduce fast and robust textural features that allow retrieving images with similar scenes comprising colour textured objects viewed with different illumination. The proposed textural features that are invariant to illumination spectrum and extremely robust to illumination direction. They require only a single training image per texture and no knowledge of illumination direction, brightness or spectrum. These feature utilises utilise illumination invariant features extracted from three different Markov random field (MRF) based texture representations.
dcterms:title
Illumination Invariants Based on Markov Random Fields Illumination Invariants Based on Markov Random Fields
skos:prefLabel
Illumination Invariants Based on Markov Random Fields Illumination Invariants Based on Markov Random Fields
skos:notation
RIV/67985556:_____/10:00343263!RIV11-MSM-67985556
n3:aktivita
n12:P n12:Z
n3:aktivity
P(1M0572), P(2C06019), P(GA102/08/0593), Z(AV0Z10750506)
n3:dodaniDat
n13:2011
n3:domaciTvurceVysledku
n15:2890542 n15:1555170
n3:druhVysledku
n8:C
n3:duvernostUdaju
n14:S
n3:entitaPredkladatele
n4:predkladatel
n3:idSjednocenehoVysledku
262849
n3:idVysledku
RIV/67985556:_____/10:00343263
n3:jazykVysledku
n5:eng
n3:klicovaSlova
illumination invariants; textural features; Markov random fields
n3:klicoveSlovo
n9:illumination%20invariants n9:Markov%20random%20fields n9:textural%20features
n3:kontrolniKodProRIV
[105E36A06890]
n3:mistoVydani
Vukovar, Croatia
n3:nazevZdroje
Pattern Recognition, Recent Advances
n3:obor
n20:BD
n3:pocetDomacichTvurcuVysledku
2
n3:pocetStranKnihy
524
n3:pocetTvurcuVysledku
2
n3:projekt
n7:1M0572 n7:GA102%2F08%2F0593 n7:2C06019
n3:rokUplatneniVysledku
n13:2010
n3:tvurceVysledku
Haindl, Michal Vácha, Pavel
n3:zamer
n19:AV0Z10750506
s:numberOfPages
20
n10:hasPublisher
In-Teh
n17:isbn
978-953-7619-90-9