Multifractal tools for image processing

Abstract : A large class of segmentation processes is based on edge detection, often performed by gradient computation. This is reliable when one can approximate the signal by a differentiable function, which is not the case for singularities such as corners or junctions. Besides, one has to use different filters to detect step-edge or line singularity model. We present a method to detect those singularities based on multifractal theory. This approach presents several advantages: it allows to do all the computations directly on the discrete signal, without any underlying regularity hypothesis; we obtain a set of low level tools able to detect any kind of singularity; this method is reliable on natural images, as shown by a complete study of noise effect and examples on natural scenes.
Type de document :
Communication dans un congrès
Kjell Arild Hogda and Bjorn Braathen and Karsten Heia. SCIA'93 : 8th Scandinavian Conference on Image Analysis, May 1993, Tromso, Norway. IAPR, pp.209-216, 1993
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https://hal.inria.fr/inria-00613998
Contributeur : Lisandro Fermin <>
Soumis le : lundi 8 août 2011 - 15:04:17
Dernière modification le : vendredi 25 mai 2018 - 12:02:06

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  • HAL Id : inria-00613998, version 1

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Jacques Lévy-Vehel, Jean-Paul Berroir. Multifractal tools for image processing. Kjell Arild Hogda and Bjorn Braathen and Karsten Heia. SCIA'93 : 8th Scandinavian Conference on Image Analysis, May 1993, Tromso, Norway. IAPR, pp.209-216, 1993. 〈inria-00613998〉

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