Abstract
An appropriate image representation induces some good image treatment algorithms. Hypergraph theory is a theory of finite combinatorial sets, modeling a lot of problems of operational research and combinatorial optimization. Hypergraphs are now used in many domains such as chemistry, engineering and image processing. We present an overview of a hypergraph-based picture representation giving much application in picture manipulation, analysis and restoration: the Image Adaptive Neighborhood Hypergraph (IANH). With the IANH it is possible to build powerful noise detection an elimination algorithm, but also to make some edges detection or some image segmentation. IANH has various applications and this paper presents a survey of them.
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© 2005 Springer-Verlag Berlin Heidelberg
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Bretto, A., Gillibert, L. (2005). Hypergraph-Based Image Representation. In: Brun, L., Vento, M. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2005. Lecture Notes in Computer Science, vol 3434. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31988-7_1
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DOI: https://doi.org/10.1007/978-3-540-31988-7_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25270-2
Online ISBN: 978-3-540-31988-7
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