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Communication dans un congrès

Advanced statistical matrices for texture characterization: Application to DNA chromatin and microtubule network classification

Abstract : This paper presents significant improvements of Gray Level Size Zone Matrix (GLSZM) which is a bivariate statistical representation of texture, based on the co-occurrences of size/intensity of each flat zone (connected pixels of the same gray level). The first improvement is a multi-scale extension of the matrix which merges various quantizations of gray levels. A second alternative is proposed to take into account radial distribution of zone intensities. The third variant is a generalization of the matrix structure which allows to analyze fibrous textures, by changing the pair intensity/size for the pair length/orientation of each region. The interest of these improved descriptors is illustrated by texture classification problems arising from quantitative cell biology.
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Communication dans un congrès
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https://hal-mines-paristech.archives-ouvertes.fr/hal-00833529
Contributeur : Bibliothèque Mines Paristech <>
Soumis le : jeudi 13 juin 2013 - 00:16:39
Dernière modification le : jeudi 24 septembre 2020 - 16:38:03

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Guillaume Thibault, Jesus Angulo, Fernand Meyer. Advanced statistical matrices for texture characterization: Application to DNA chromatin and microtubule network classification. 18th IEEE International Conference on Image Processing (ICIP), Sep 2011, Bruxelles, Belgium. pp.53-57, ⟨10.1109/ICIP.2011.6116401⟩. ⟨hal-00833529⟩

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