Search results for "hypergraph"
showing 3 items of 13 documents
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.
Domain Specific Knowledge Representation for an Intelligent Tutoring System to Teach Algebraic Reasoning
2012
Translation of word problems into symbolic notation is one of the most challenging steps in learning the algebraic method. This paper describes a domain-specific knowledge representation mechanism to support Intelligent Tutoring Systems (ITS) which focus on this stage of the problem solving process. The description language proposed is based on the concept of a hypergraph and makes it possible to simultaneously a) represent all potential algebraic solutions to a given word problem; b) keep track of the student's actions; c) provide automatic remediation; and d) unequivocally determine the current state of the resolution process. An experimental evaluation with students at a public school su…
Spatiocolorimetric neighborhood hypergraph and Image Processing Applications : Noise Removal and Edge Detection.
2004
In this document, we are interested in image modeling by the means of the hypergraph theory. Our contribution is essentially centered on the determination of the properties resulting from this theory and on the analysis from their adequacy with image problems, particularly edge and noise detection.First, we study the image spatiocolorimetric neighborhood hypergraph representation. Three representations are respectively presented incorporating global properties, local properties and similarity functions. Then, we use the hypergraph properties generated by the representation in order to define the structural models of noise and edge. This enables us to deduce the algorithms of noise suppressi…