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
Link To Document :
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