Kolmogorov Superposition Theorem and its application to multivariate function decompositions and image representation

Abstract : In this paper, we present the problem of multivariate function decompositions into sums and compositions of monovariate functions. We recall that such a decomposition exists in the Kolmogorov's superposition theorem, and we present two of the most recent constructive algorithms of these monovariate functions. We first present the algorithm proposed by Sprecher, then the algorithm proposed by Igelnik, and we present several results of decomposition for gray level images. Our goal is to adapt and apply the superposition theorem to image processing, i.e. to decompose an image into simpler functions using Kolmogorov superpositions. We synthetise our observations, before presenting several research perspectives.
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Communication dans un congrès
IEEE Computer Society Washington, DC, USA. IEEE conference on Signal-Image Technology & Internet-Based System, Nov 2008, Bali, Indonesia. pp.344-351, 2008
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Contributeur : Pierre-Emmanuel Leni <>
Soumis le : jeudi 20 octobre 2011 - 11:26:25
Dernière modification le : jeudi 20 octobre 2011 - 11:29:28
Document(s) archivé(s) le : samedi 21 janvier 2012 - 02:26:20

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Pierre-Emmanuel Leni, Yohan Fougerolle, Frederic Truchetet. Kolmogorov Superposition Theorem and its application to multivariate function decompositions and image representation. IEEE Computer Society Washington, DC, USA. IEEE conference on Signal-Image Technology & Internet-Based System, Nov 2008, Bali, Indonesia. pp.344-351, 2008. <hal-00634095>

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