Volume 239, Issue 2 pp. 159-166

Texture classification by statistical learning from morphological image processing: application to metallic surfaces

A. CORD,

A. CORD

UniverSud, LIVIC, INRETS–LCPC, Versailles, France

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F. BACH,

F. BACH

Laboratoire d'Informatique de l'Ecole Normale Superieure (CNRS/ENS/INRIA UMR 8548), Paris, France

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D. JEULIN,

D. JEULIN

Centre de Morphologie Mathématique (CMM), Mathématiques et Systmes, Fontainebleau CEDEX, France

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A. CORD,

A. CORD

UniverSud, LIVIC, INRETS–LCPC, Versailles, France

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F. BACH,

F. BACH

Laboratoire d'Informatique de l'Ecole Normale Superieure (CNRS/ENS/INRIA UMR 8548), Paris, France

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D. JEULIN,

D. JEULIN

Centre de Morphologie Mathématique (CMM), Mathématiques et Systmes, Fontainebleau CEDEX, France

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First published: 14 July 2010

https://doi.org/10.1111/j.1365-2818.2010.03365.x

Citations: 32

D. Jeulin, Centre de Morphologie Mathématique (CMM), Mathématiques et Systémes, Mines ParisTech 35 rue Saint Honoré, 77305 Fontainebleau CEDEX, France. e-mail: [email protected]

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Summary

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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Volume239, Issue2

August 2010

Pages 159-166

Texture classification by statistical learning from morphological image processing: application to metallic surfaces

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Texture classification by statistical learning from morphological image processing: application to metallic surfaces

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