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

CORD, A ; BACH, F ; JEULIN, D

Journal of microscopy (Oxford), 2010-08, Vol.239 (2), p.159-166 [Periódico revisado por pares]

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Título:
Texture classification by statistical learning from morphological image processing: application to metallic surfaces
Autor: CORD, A ; BACH, F ; JEULIN, D
Assuntos: Classification ; Engineering Sciences ; Learning ; Materials ; Mathematical morphology ; Microscopy ; Pixels ; rough surfaces ; Small scale ; statistical learning ; Surface layer ; Texture ; texture classification ; Variability
É parte de: Journal of microscopy (Oxford), 2010-08, Vol.239 (2), p.159-166
Notas: http://dx.doi.org/10.1111/j.1365-2818.2010.03365.x
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Descrição: 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.
Editor: Oxford, UK: Blackwell Publishing Ltd
Idioma: Inglês

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