Texture Classification By Statistical Learning From Morphological Image Processing: Application To Metallic Surfaces

Aurélien Cord 1 Francis Bach 2, 3 Dominique Jeulin 4
3 SIERRA - Statistical Machine Learning and Parsimony
DI-ENS - Département d'informatique de l'École normale supérieure, ENS Paris - École normale supérieure - Paris, Inria Paris-Rocquencourt, CNRS - Centre National de la Recherche Scientifique : UMR8548
Abstract : A classification method based on textural information for metallic surfaces displaying complex random patterns is proposed. Because these kinds of textures show fluctuations at a small scale and some uniformity at a larger scale, a probabilistic approach is followed, considering textural variations as realizations of random functions. Taking into account information of pixel neighbourhoods, the texture for each pixel is described at different scales. By means of statistical learning, the most relevant textural descriptors are selected for each application. The performance of this approach is established on a real data set of steel surfaces.
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Article dans une revue
Journal of Microscopy, Wiley, 2010, 239 (2), pp.159-166. <10.1111/j.1365-2818.2010.03365.x>
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https://hal-mines-paristech.archives-ouvertes.fr/hal-00836006
Contributeur : Sylvie Lavigne <>
Soumis le : jeudi 20 juin 2013 - 11:38:49
Dernière modification le : mardi 12 septembre 2017 - 11:41:31

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Aurélien Cord, Francis Bach, Dominique Jeulin. Texture Classification By Statistical Learning From Morphological Image Processing: Application To Metallic Surfaces. Journal of Microscopy, Wiley, 2010, 239 (2), pp.159-166. <10.1111/j.1365-2818.2010.03365.x>. <hal-00836006>

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