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Description
  • A fast and robust type of unsupervised multispectral texture segmentation method with unknown number of classes is presented. Single decorrelated monospectral texture factors are represented by four local autoregressive random field models recursively evaluated for each pixel and for each spectral band. The segmentation algorithm is based on the underlying Gaussian mixture model and starts with an oversegmented initial estimation which is adaptively modified until the optimal number of segments is found.
  • A fast and robust type of unsupervised multispectral texture segmentation method with unknown number of classes is presented. Single decorrelated monospectral texture factors are represented by four local autoregressive random field models recursively evaluated for each pixel and for each spectral band. The segmentation algorithm is based on the underlying Gaussian mixture model and starts with an oversegmented initial estimation which is adaptively modified until the optimal number of segments is found. (en)
  • Článek představuje novou robustní metodu segmentace multispektrálních textur s neznámým počtem tříd. Jednotlivé dekorelované monospektrální složky jsou reprezentovány čtyřmi lokálními modely autoregresivních náhodných polí, které jsou rekurzivně identifikovány pro každý pixel a pro každé spektrální pásmo. Segmentační algoritmus je založen na gaussovském směsovém modelu a začíná s přesegmentovaným počátečním odhadem, který se adaptivně mění až do dosažení optimálního počtu segmentů. (cs)
Title
  • Colour texture segmentation using modelling approach
  • Colour texture segmentation using modelling approach (en)
  • Segmentace barevných textur pomocí modelovacího přístupu (cs)
skos:prefLabel
  • Colour texture segmentation using modelling approach
  • Colour texture segmentation using modelling approach (en)
  • Segmentace barevných textur pomocí modelovacího přístupu (cs)
skos:notation
  • RIV/67985556:_____/05:00411406!RIV06-AV0-67985556
http://linked.open.../vavai/riv/strany
  • 484;491
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(1ET400750407), P(IAA2075302), Z(AV0Z10750506)
http://linked.open...iv/cisloPeriodika
  • -
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
  • 515687
http://linked.open...ai/riv/idVysledku
  • RIV/67985556:_____/05:00411406
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • colour texture segmentation; image models; segmentation benchmark (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • DE - Spolková republika Německo
http://linked.open...ontrolniKodProRIV
  • [17670A1FE1FF]
http://linked.open...i/riv/nazevZdroje
  • Pattern Recognition and Image Analysis
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...v/svazekPeriodika
  • 3687
http://linked.open...iv/tvurceVysledku
  • Haindl, Michal
  • Mikeš, Stanislav
http://linked.open...n/vavai/riv/zamer
issn
  • 0302-9743
number of pages
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