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  • Image segmentation is one of the classic problems in the computer vision field. Although a lot of successful operators and algorithms have been proposed, fuzzy image segmentation does not always achieve satisfactory results. This paper is inspired by Positive Selection Algorithm and Negative Selection Algorithm and, is based on the mechanism and process where T-cell is activated by the MHC molecule. A new positive selection algorithm is introduced which establishes so-called templates set for immune detection. This algorithm is based on processing of image information represented as a gray value statistic rather than arithmetic gradient formulation. It is comprised of a template set not just a single template. Therefore it gives good results for different images. The presented algorithm is used for image segmentation into objects, background and fuzzy edge in fuzzy infrared images.
  • Image segmentation is one of the classic problems in the computer vision field. Although a lot of successful operators and algorithms have been proposed, fuzzy image segmentation does not always achieve satisfactory results. This paper is inspired by Positive Selection Algorithm and Negative Selection Algorithm and, is based on the mechanism and process where T-cell is activated by the MHC molecule. A new positive selection algorithm is introduced which establishes so-called templates set for immune detection. This algorithm is based on processing of image information represented as a gray value statistic rather than arithmetic gradient formulation. It is comprised of a template set not just a single template. Therefore it gives good results for different images. The presented algorithm is used for image segmentation into objects, background and fuzzy edge in fuzzy infrared images. (en)
Title
  • A Novel Immune Image Template Set for Fuzzy Image Segmentation and its Application Research
  • A Novel Immune Image Template Set for Fuzzy Image Segmentation and its Application Research (en)
skos:prefLabel
  • A Novel Immune Image Template Set for Fuzzy Image Segmentation and its Application Research
  • A Novel Immune Image Template Set for Fuzzy Image Segmentation and its Application Research (en)
skos:notation
  • RIV/00216305:26220/11:PU95153!RIV12-MSM-26220___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(ME10123), S, Z(MSM0021630513)
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
  • 184020
http://linked.open...ai/riv/idVysledku
  • RIV/00216305:26220/11:PU95153
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Positive Selection, Image Segmentation, Fuzzy Infrared Image, Artificial Immune. (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [E582E4CB25E6]
http://linked.open...v/mistoKonaniAkce
  • Budapest
http://linked.open...i/riv/mistoVydani
  • Neuveden
http://linked.open...i/riv/nazevZdroje
  • 34th International Conference on Telecommunications and Signal Processing (TSP 2011)
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
  • Burget, Radim
  • Fu, Dongmei
  • Qui, Xintao
  • Yang, Tao
  • Říha, Kamil
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
  • Neuveden
https://schema.org/isbn
  • 978-1-4577-1409-2
http://localhost/t...ganizacniJednotka
  • 26220
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