General adaptive neighborhood mathematical morphology

Authors:
Jean-Charles Pinoli;Johan Debayle
Affiliations:
Ecole Nationale Supérieure des Mines de Saint-Etienne, LPMG, UMR, CNRS, Saint-Etienne Cedex 2, France;Ecole Nationale Supérieure des Mines de Saint-Etienne, LPMG, UMR, CNRS, Saint-Etienne Cedex 2, France
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

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Abstract

This paper aims to present a novel framework, entitled General Adaptive Neighborhood Image Processing (GANIP), focusing on the area of adaptive morphology. The usual fixed-shape structuring elements required in Mathematical Morphology (MM) are substituted by adaptive (GAN-based) spatial structuring elements. GANIP and MM results to the so-called General Adaptive Neighborhood Mathematical Morphology (GANMM). Several GANMM-based image filters are defined. They satisfy strong morphological and topological properties such as connectedness. The practical results in the fields of image restoration and image enhancement confirm and highlight the theoretical advantages of the GANMM approach.