Segmentation of EIT images using fuzzy clustering. A preliminary study

Abstract : We present a new application of a signal processing technique called fuzzy clustering for automatic identification of various structures seen in EIT images. This technique gives for each pixel a probability of belonging to a particular class. By assigning each pixel to the class for which it has the greatest probability of belonging, we obtain image segmentations. In EIT 19.5 nm images we distinguish the Quiet Sun, Coronal Holes, and the Active Regions, whereas in EIT 30.4 nm we extract the plages and part of the network boundaries. We also show how a multiwavelength approach leads to an improved segmen-tation of the different coronal structures.
Type de document :
Communication dans un congrès
Proceedings of European SPM-11, Sep 2005, Leuwen, Belgium
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https://hal-clermont-univ.archives-ouvertes.fr/hal-01622942
Contributeur : Vincent Barra <>
Soumis le : mardi 24 octobre 2017 - 18:56:40
Dernière modification le : mercredi 31 janvier 2018 - 18:18:05

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  • HAL Id : hal-01622942, version 1

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Vincent Barra, Véronique Delouille, Jean-François Hochedez, Pierre Chainais. Segmentation of EIT images using fuzzy clustering. A preliminary study. Proceedings of European SPM-11, Sep 2005, Leuwen, Belgium. 〈hal-01622942〉

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