General adaptive neighborhood image restoration, enhancement and segmentation

Authors:
Johan Debayle;Yann Gavet;Jean-Charles Pinoli
Affiliations:
Laboratoire LPMG, UMR CNRS 5148, Ecole Nationale Supérieure des Mines de Saint-Etienne, Centre Ingénierie et Santé (CIS), Saint-Etienne, France;Laboratoire LPMG, UMR CNRS 5148, Ecole Nationale Supérieure des Mines de Saint-Etienne, Centre Ingénierie et Santé (CIS), Saint-Etienne, France;Laboratoire LPMG, UMR CNRS 5148, Ecole Nationale Supérieure des Mines de Saint-Etienne, Centre Ingénierie et Santé (CIS), Saint-Etienne, France
Venue:
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
Year:
2006

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Abstract

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.