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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. (en)
Title
  • A Psychophysical Evaluation of Texture Degradation Descriptors
  • A Psychophysical Evaluation of Texture Degradation Descriptors (en)
skos:prefLabel
  • A Psychophysical Evaluation of Texture Degradation Descriptors
  • A Psychophysical Evaluation of Texture Degradation Descriptors (en)
skos:notation
  • RIV/67985556:_____/10:00346556!RIV11-MSM-67985556
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(1M0572), P(GA102/08/0593), Z(AV0Z10750506)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 244895
http://linked.open...ai/riv/idVysledku
  • RIV/67985556:_____/10:00346556
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • texture; degradation; statistical features; BTF; psychophysics (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [EE9860D83126]
http://linked.open...v/mistoKonaniAkce
  • Cesme, Izmir
http://linked.open...i/riv/mistoVydani
  • Berlin / Heidelberg
http://linked.open...i/riv/nazevZdroje
  • Structural, Syntactic, and Statistical Pattern Recognition
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Filip, Jiří
  • Haindl, Michal
  • Vácha, Pavel
  • Green, P. R.
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
number of pages
http://purl.org/ne...btex#hasPublisher
  • Springer-Verlag. (Berlin; Heidelberg)
https://schema.org/isbn
  • 978-3-642-14979-5
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