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Proceedings Paper

Pyramid of hypergraphs for image processing
Author(s): Hubert Konik; Alain Bretto; Bernard Laget
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Paper Abstract

There is a lack of general models in image processing, particularly for image segmentation. Actually, each treatment must combine several classical features, such as grey level values, neighborhood and spatial distribution. In fact, an image can be studied both as an aspect of geometry and as an aspect of combinatorics. Here, to each digital image we associate a neighborhood hypergraph. This general model is in fact clearly adapted to include grey level and neighborhood informations, and particularly for image segmentation. Moreover, the pyramid constitutes an efficient tool in image analysis, simulating the human vision in its attention focusing, through an individual and a contextual analysis of each region. This multiresolution scheme allows simultaneously relevant regions detection and detailed delineation. Then, combining the two approaches, a hypergraph segmentation is associated at each level of the pyramid. Finally, we use the evolution of this pyramid of hypergraphs for image segmentation and more generally modelization.

Paper Details

Date Published: 4 April 1997
PDF: 9 pages
Proc. SPIE 3026, Nonlinear Image Processing VIII, (4 April 1997); doi: 10.1117/12.271130
Show Author Affiliations
Hubert Konik, Univ. Jean Monnet (France)
Alain Bretto, Univ. Jean Monnet (France)
Bernard Laget, Univ. Jean Monnet (France)


Published in SPIE Proceedings Vol. 3026:
Nonlinear Image Processing VIII
Edward R. Dougherty; Jaakko T. Astola, Editor(s)

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