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Statements

Subject Item
n2:RIV%2F67985556%3A_____%2F10%3A00346556%21RIV11-MSM-67985556
rdf:type
skos:Concept n13:Vysledek
dcterms:description
Delivering a digital realistic appearance of materials is one of the most difficult tasks of computer vision. Accurate representation of surface texture can be obtained by means of view and illumination dependent textures. However, this kind of appearance representation produces massive datasets so their compression is inevitable. For optimal visual performance of compression methods, their parameters should be set dependently on the actual material. We propose a set of statistical descriptors motivated by standard textural features, and psychophysically evaluate their performance on three subtle artificial texture visual degradations. We tested the five types of descriptors on five different textures and combination of thirteen surface shapes and two illuminations. We have found that descriptors based on two-dimensional causal auto-regressive model, have the highest correlation with the psychophysical results. Delivering a digital realistic appearance of materials is one of the most difficult tasks of computer vision. Accurate representation of surface texture can be obtained by means of view and illumination dependent textures. However, this kind of appearance representation produces massive datasets so their compression is inevitable. For optimal visual performance of compression methods, their parameters should be set dependently on the actual material. We propose a set of statistical descriptors motivated by standard textural features, and psychophysically evaluate their performance on three subtle artificial texture visual degradations. We tested the five types of descriptors on five different textures and combination of thirteen surface shapes and two illuminations. We have found that descriptors based on two-dimensional causal auto-regressive model, have the highest correlation with the psychophysical results.
dcterms:title
A Psychophysical Evaluation of Texture Degradation Descriptors A Psychophysical Evaluation of Texture Degradation Descriptors
skos:prefLabel
A Psychophysical Evaluation of Texture Degradation Descriptors A Psychophysical Evaluation of Texture Degradation Descriptors
skos:notation
RIV/67985556:_____/10:00346556!RIV11-MSM-67985556
n3:aktivita
n9:Z n9:P
n3:aktivity
P(1M0572), P(GA102/08/0593), Z(AV0Z10750506)
n3:dodaniDat
n18:2011
n3:domaciTvurceVysledku
n4:1555170 n4:2007673 n4:2890542
n3:druhVysledku
n15:D
n3:duvernostUdaju
n20:S
n3:entitaPredkladatele
n6:predkladatel
n3:idSjednocenehoVysledku
244895
n3:idVysledku
RIV/67985556:_____/10:00346556
n3:jazykVysledku
n16:eng
n3:klicovaSlova
texture; degradation; statistical features; BTF; psychophysics
n3:klicoveSlovo
n7:statistical%20features n7:texture n7:degradation n7:psychophysics n7:BTF
n3:kontrolniKodProRIV
[EE9860D83126]
n3:mistoKonaniAkce
Cesme, Izmir
n3:mistoVydani
Berlin / Heidelberg
n3:nazevZdroje
Structural, Syntactic, and Statistical Pattern Recognition
n3:obor
n11:BD
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
4
n3:projekt
n8:1M0572 n8:GA102%2F08%2F0593
n3:rokUplatneniVysledku
n18:2010
n3:tvurceVysledku
Vácha, Pavel Filip, Jiří Green, P. R. Haindl, Michal
n3:typAkce
n21:WRD
n3:zahajeniAkce
2010-08-18+02:00
n3:zamer
n14:AV0Z10750506
s:numberOfPages
11
n19:hasPublisher
Springer-Verlag. (Berlin; Heidelberg)
n17:isbn
978-3-642-14979-5