• DocumentCode
    249441
  • Title

    Locally Gaussian exemplar based texture synthesis

  • Author

    Raad, Lara ; Desolneux, Agnes ; Morel, Jean-Michel

  • Author_Institution
    CMLA, Ecole Normale Super. de Cachan, Cachan, France
  • fYear
    2014
  • fDate
    27-30 Oct. 2014
  • Firstpage
    4667
  • Lastpage
    4671
  • Abstract
    The main approaches to texture modeling are the statistical psychophysically inspired model and the patch-based model. In the first model the texture is characterized by a sophisticated statistical signature. The associated sampling algorithm estimates this signature from the example and produces a genuinely different texture. This texture nevertheless often loses accuracy. The second model boils down to a clever copy-paste procedure, which stitches verbatim copies of large regions of the example. We propose in this communication to involve a locally Gaussian texture model in the patch space. It permits to synthesize textures that are everywhere different from the original but with better quality than the purely statistical methods.
  • Keywords
    image texture; statistical analysis; clever copy-paste procedure; locally Gaussian exemplar based texture synthesis; patch-based model; sampling algorithm; statistical psychophysically inspired model; Biological system modeling; Gaussian distribution; Image denoising; Image edge detection; Visualization; Gaussian Modeling; Image Patches; Texture Synthesis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Image Processing (ICIP), 2014 IEEE International Conference on
  • Conference_Location
    Paris
  • Type

    conf

  • DOI
    10.1109/ICIP.2014.7025946
  • Filename
    7025946