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Statements

Subject Item
n2:RIV%2F67985556%3A_____%2F12%3A00380288%21RIV13-MSM-67985556
rdf:type
skos:Concept n4:Vysledek
dcterms:description
We provide a thorough experimental evaluation of several state-of-the-art textural features on four representative and extensive image data/-bases. Each of the experimental textural databases ALOT, Bonn BTF, UEA Uncalibrated, and KTH-TIPS2 aims at specific part of realistic acquisition conditions of surface materials represented as multispectral textures. The extensive experimental evaluation proves the outstanding reliable and robust performance of efficient Markovian textural features analytically derived from a wide-sense Markov random field causal model. These features systematically outperform leading Gabor, Opponent Gabor, LBP, and LBP-HF alternatives. Moreover, they even allow successful recognition of arbitrary illuminated samples using a single training image per material. Our features are successfully applied also for the recent most advanced textural representation in the form of 7-dimensional Bidirectional Texture Function (BTF). We provide a thorough experimental evaluation of several state-of-the-art textural features on four representative and extensive image data/-bases. Each of the experimental textural databases ALOT, Bonn BTF, UEA Uncalibrated, and KTH-TIPS2 aims at specific part of realistic acquisition conditions of surface materials represented as multispectral textures. The extensive experimental evaluation proves the outstanding reliable and robust performance of efficient Markovian textural features analytically derived from a wide-sense Markov random field causal model. These features systematically outperform leading Gabor, Opponent Gabor, LBP, and LBP-HF alternatives. Moreover, they even allow successful recognition of arbitrary illuminated samples using a single training image per material. Our features are successfully applied also for the recent most advanced textural representation in the form of 7-dimensional Bidirectional Texture Function (BTF).
dcterms:title
Texture Recognition using Robust Markovian Features Texture Recognition using Robust Markovian Features
skos:prefLabel
Texture Recognition using Robust Markovian Features Texture Recognition using Robust Markovian Features
skos:notation
RIV/67985556:_____/12:00380288!RIV13-MSM-67985556
n4:predkladatel
n5:ico%3A67985556
n3:aktivita
n8:I n8:P
n3:aktivity
I, P(1M0572), P(GA102/08/0593), P(GAP103/11/0335)
n3:dodaniDat
n15:2013
n3:domaciTvurceVysledku
n12:2890542 n12:1555170
n3:druhVysledku
n21:D
n3:duvernostUdaju
n13:S
n3:entitaPredkladatele
n17:predkladatel
n3:idSjednocenehoVysledku
174151
n3:idVysledku
RIV/67985556:_____/12:00380288
n3:jazykVysledku
n6:eng
n3:klicovaSlova
texture recognition; illumination invariance; Markov random fields; Bidirectional Texture Function; textural databases
n3:klicoveSlovo
n11:Bidirectional%20Texture%20Function n11:textural%20databases n11:texture%20recognition n11:illumination%20invariance n11:Markov%20random%20fields
n3:kontrolniKodProRIV
[1CBBABE89424]
n3:mistoKonaniAkce
Pisa
n3:mistoVydani
Berlin
n3:nazevZdroje
Computational Intelligence for Multimedia Understanding
n3:obor
n18:BD
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:projekt
n7:1M0572 n7:GAP103%2F11%2F0335 n7:GA102%2F08%2F0593
n3:rokUplatneniVysledku
n15:2012
n3:tvurceVysledku
Vácha, Pavel Haindl, Michal
n3:typAkce
n20:WRD
n3:zahajeniAkce
2011-12-13+01:00
s:numberOfPages
12
n16:doi
10.1007/978-3-642-32436-9_11
n22:hasPublisher
Springer-Verlag
n19:isbn
978-3-642-32435-2