Pyramid of hypergraphs for image processing

Hubert Konik; Alain Bretto; Bernard Laget

doi:10.1117/12.271130

4 April 1997 Pyramid of hypergraphs for image processing

Hubert Konik, Alain Bretto, Bernard Laget

Proceedings Volume 3026, Nonlinear Image Processing VIII; (1997) https://doi.org/10.1117/12.271130
Event: Electronic Imaging '97, 1997, San Jose, CA, United States

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.

Citation Download Citation

Hubert Konik, Alain Bretto, and Bernard Laget "Pyramid of hypergraphs for image processing", Proc. SPIE 3026, Nonlinear Image Processing VIII, (4 April 1997); https://doi.org/10.1117/12.271130

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KEYWORDS

Image segmentation

Image processing

Clouds

Visual process modeling

Image analysis

Human vision and color perception

Image compression

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