General adaptive neighborhood image restoration, enhancement and segmentation

被引:0
|
作者
Debayle, Johan [1 ]
Gavet, Yann [1 ]
Pinoli, Jean-Charles [1 ]
机构
[1] Ecole Natl Super Mines, CIS, Lab LPMG, CNRS,UMR 5148, F-42023 St Etienne 2, France
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中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper aims to outline the General Adaptive Neighborhood Image Processing (GANIP) approach [1-3], which has been recently introduced. An intensity image is represented with a set of local neighborhoods defined for each point of the image to be studied. These so-called General Adaptive Neighborhoods (GANs) are simultaneously adaptive with the spatial structures, the analyzing scales and the physical settings of the image to be addressed and/or the human visual system. After a brief theoretical introductory survey, the GANIP approach will be successfully applied on real application examples in image restoration, enhancement and segmentation.
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页码:29 / 40
页数:12
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