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Description
  • Segmentation is the fundamental process which partitions a data space into meaningful salient regions. Image segmentation essentially affects the overall performance of any automated image analysis system thus its quality is of the utmost importance. Image regions, homogeneous with respect to some usually textural or colour measure, which result from a segmentation algorithm are analysed in subsequent interpretation steps. Several new unsupervised multispectral texture segmentation methods based on underlying Markovian spatial models with unknown number of classes are presented in the chapter. The performances of the presented methods are extensively tested on the Prague segmentation benchmark using the commonest segmentation criteria and compares favourably with several alternative texture segmentation methods.
  • Segmentation is the fundamental process which partitions a data space into meaningful salient regions. Image segmentation essentially affects the overall performance of any automated image analysis system thus its quality is of the utmost importance. Image regions, homogeneous with respect to some usually textural or colour measure, which result from a segmentation algorithm are analysed in subsequent interpretation steps. Several new unsupervised multispectral texture segmentation methods based on underlying Markovian spatial models with unknown number of classes are presented in the chapter. The performances of the presented methods are extensively tested on the Prague segmentation benchmark using the commonest segmentation criteria and compares favourably with several alternative texture segmentation methods. (en)
  • Segmentace je základní proces, který rozděluje datový prostor na smysluplné charakteristické podprostory. Segmentace obrazu zásadně ovlivňuje celkovou spolehlivost každého automatického systému obrazové analýzy. Oblasti obrazu, homogenní vzhledem k nějaké, obvykle texturní nebo spektrální míře a které jsou výsledkem segmentace, jsou následně analyzovány v interpretační části metod. Kapitola popisuje několik nových metod neřízené segmentace textur, založených na markovských prostorových modelech s neznámým počtem tříd. Tyto metody jsou intenzivně testovány na Pražském segmentačním benchmarku při použití běžných segmentačních kriterií. Výsledky těchto komplexních testů ukazují, že naše metody předčí některé publikované alternativní segmentační metody textur. (cs)
Title
  • Unsupervised Texture Segmentation
  • Neřízená segmentace textur (cs)
  • Unsupervised Texture Segmentation (en)
skos:prefLabel
  • Unsupervised Texture Segmentation
  • Neřízená segmentace textur (cs)
  • Unsupervised Texture Segmentation (en)
skos:notation
  • RIV/67985556:_____/08:00317725!RIV09-GA0-67985556
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(1ET400750407), P(1M0572), P(2C06019), 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
  • 401495
http://linked.open...ai/riv/idVysledku
  • RIV/67985556:_____/08:00317725
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • texture segmentation; image segmentation; unsupervised segmentation (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [590E262002E3]
http://linked.open...i/riv/mistoVydani
  • Vienna
http://linked.open...i/riv/nazevZdroje
  • Pattern Recognition
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...v/pocetStranKnihy
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Haindl, Michal
  • Mikeš, Stanislav
http://linked.open...n/vavai/riv/zamer
number of pages
http://purl.org/ne...btex#hasPublisher
  • In-Tech
https://schema.org/isbn
  • 978-953-7619-24-4
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