General Adaptive Neighborhood Image Restoration, Enhancement and Segmentation

Johan Debayle (1, 2, 3) , Yann Gavet (1, 2, 3) , Jean-Charles Pinoli (1, 2, 3)

1 CIS-ENSMSE - Centre Ingénierie et Santé
2 DIS-ENSMSE - Département Imagerie et Statistiques
3 LPMG-EMSE - Laboratoire des Procédés en Milieux Granulaires

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.

Mots clés: image representation; general adaptive neighborhood; mathematical morphology; image filtering

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Johan Debayle, Yann Gavet, Jean-Charles Pinoli. General Adaptive Neighborhood Image Restoration, Enhancement and Segmentation. Image Analysis and Recognition, 2006, France. pp.29-40. ⟨hal-00128111⟩