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Description
  • This paper describes a method for texture based segmentation. Texture features are extracted by applying a bank of Gabor filters using two-sided convolution strategy. Probability texture model is represented by Gaussian mixture that is trained with the Expectation-maximization algorithm. Texture similarity, obtained this way, is used like the input of a Graph cut method. We show that the combination of texture analysis and the Graph cut method produce good results.
  • This paper describes a method for texture based segmentation. Texture features are extracted by applying a bank of Gabor filters using two-sided convolution strategy. Probability texture model is represented by Gaussian mixture that is trained with the Expectation-maximization algorithm. Texture similarity, obtained this way, is used like the input of a Graph cut method. We show that the combination of texture analysis and the Graph cut method produce good results. (en)
Title
  • Texture Based Segmentation Using Graph Cut and Gabor Filters
  • Texture Based Segmentation Using Graph Cut and Gabor Filters (en)
skos:prefLabel
  • Texture Based Segmentation Using Graph Cut and Gabor Filters
  • Texture Based Segmentation Using Graph Cut and Gabor Filters (en)
skos:notation
  • RIV/49777513:23520/11:43898223!RIV12-MSM-23520___
http://linked.open...avai/riv/aktivita
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  • 235051
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  • RIV/49777513:23520/11:43898223
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  • Texture similarity; Texture models; Texture features; Texture analysis; Graph cut; Gaussian mixtures; Gabor filter; Expectation-maximization algorithms (en)
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  • DE - Spolková republika Německo
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  • [72A0D45F2D1B]
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  • Pattern Recognition and Image Analysis
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  • 21
http://linked.open...iv/tvurceVysledku
  • Železný, Miloš
  • Jiřík, Miroslav
  • Ryba, Tomáš
issn
  • 1054-6618
number of pages
http://bibframe.org/vocab/doi
  • 10.1134/S105466181102043X
http://localhost/t...ganizacniJednotka
  • 23520
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