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.
Type de document :
Communication dans un congrès
18th IEEE International Conference on Image Processing (ICIP), Sep 2011, Bruxelles, Belgium. IEEE, pp.53-57, 2011, 〈10.1109/ICIP.2011.6116401〉
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https://hal-mines-paristech.archives-ouvertes.fr/hal-00833529
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Soumis le : jeudi 13 juin 2013 - 00:16:39
Dernière modification le : vendredi 27 octobre 2017 - 17:36:02

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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. IEEE, pp.53-57, 2011, 〈10.1109/ICIP.2011.6116401〉. 〈hal-00833529〉

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