0000000000400989

AUTHOR

Alain Bretto

showing 3 related works from this author

An Efficient Algorithm for Helly Property Recognition in a Linear Hypergraph

2001

International audience; In this article we characterize bipartite graphs whose associated neighborhood hypergraphs have the Helly property. We examine incidence graphs both hypergraphs and linear hypergraphs and we give a polynomial algorithm to recognize if a linear hypergraph has the Helly property.

HypergraphProperty (philosophy)General Computer Science[ INFO.INFO-NI ] Computer Science [cs]/Networking and Internet Architecture [cs.NI]0102 computer and information sciences02 engineering and technologyComputer Science::Computational Geometry01 natural sciencesPolynomial algorithmTheoretical Computer ScienceCombinatorics[INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI][ INFO.INFO-DC ] Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC]Computer Science::Discrete Mathematics[ INFO.INFO-TI ] Computer Science [cs]/Image Processing0202 electrical engineering electronic engineering information engineeringMathematics::Metric GeometryComputingMilieux_MISCELLANEOUSMathematicsIncidence (geometry)Discrete mathematicsMathematics::CombinatoricsEfficient algorithm16. Peace & justice010201 computation theory & mathematics[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]Bipartite graph020201 artificial intelligence & image processing[INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC]Computer Science(all)Electronic Notes in Theoretical Computer Science
researchProduct

Application of Adaptive Hypergraph Model to Impulsive Noise Detection

2001

In this paper, using hypergraph theory, we introduce an image model called Adaptive Image Neighborhood Hypergraph (AINH). From this model we propose a combinatorial definition of noisy data. A detection procedure is used to classify the hyperedges either as noisy or clean data. Similar to other techniques, the proposed algorithm uses an estimation procedure to remove the effects of the noise. Extensive simulations show that the proposed scheme consistently works well in suppressing of impulsive noise.

Scheme (programming language)NoiseHypergraphSignal-to-noise ratioComputer scienceImage processingGraph theoryNoise detectionAlgorithmcomputercomputer.programming_languageImage (mathematics)
researchProduct

Hypergraph imaging: an overview

2002

Hypergraph theory as originally developed by Berge (Hypergraphe, Dunod, Paris, 1987) is a theory of finite combinatorial sets, modeling lot of problems of operational research and combinatorial optimization. This framework turns out to be very interesting for many other applications, in particular for computer vision. In this paper, we are going to survey the relationship between combinatorial sets and image processing. More precisely, we propose an overview of different applications from image hypergraph models to image analysis. It mainly focuses on the combinatorial representation of an image and shows the effectiveness of this approach to low level image processing; in particular to seg…

HypergraphTheoretical computer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONImage processingImage segmentationEdge detectionScale spaceArtificial IntelligenceComputer Science::Computer Vision and Pattern RecognitionSignal ProcessingCombinatorial optimizationComputer Vision and Pattern RecognitionRepresentation (mathematics)SoftwareMathematicsofComputing_DISCRETEMATHEMATICSFeature detection (computer vision)MathematicsPattern Recognition
researchProduct