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  • This paper describes a method for texture based segmentation. Texture features are extracted by applying a bank of Gabor filters using twosided 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 twosided 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
  • Gabor filter and graph cut based texture analysis
  • Gabor filter and graph cut based texture analysis (en)
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  • Gabor filter and graph cut based texture analysis
  • Gabor filter and graph cut based texture analysis (en)
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  • RIV/49777513:23520/12:43916384!RIV13-MSM-23520___
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  • RIV/49777513:23520/12:43916384
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  • Gabor filter, the Graph cut method (en)
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  • DE - Spolková republika Německo
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  • [B3EE22FD1079]
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  • Pattern Recognition and Image Analysis
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  • 22
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  • Železný, Miloš
  • Jiřík, Miroslav
  • Ryba, Tomáš
issn
  • 1054-6618
number of pages
http://bibframe.org/vocab/doi
  • 10.1134/S1054661812010208
http://localhost/t...ganizacniJednotka
  • 23520
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