Computing Histogram of Tensor Images using Orthogonal Series Density Estimation and Riemannian Metrics

Abstract : This paper deals with the computation of the histogram of tensor images, that is, images where at each pixel is given a n by n positive definite symmetric matrix, SPD(n). An approach based on orthogonal series density estimation is introduced, which is particularly useful for the case of measures based on Riemannian metrics. By considering SPD(n) as the space of the covariance matrices of multivariate gaussian distributions, we obtain the corresponding density estimation for the measure of both the Fisher metric and the Wasserstein metric. Experimental results on the application of such histogram estimation to DTI image segmentation, texture segmentation and texture recognition are included.
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Communication dans un congrès
22nd International Conference on Pattern Recognition (ICPR), 2014, Aug 2014, Stockholm, Sweden. Pattern Recognition (ICPR), 2014 22nd International Conference on. <http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6966883>. <10.1109/ICPR.2014.165>
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https://hal.archives-ouvertes.fr/hal-00941147
Contributeur : Emmanuel Chevallier <>
Soumis le : vendredi 11 avril 2014 - 15:19:54
Dernière modification le : mardi 12 septembre 2017 - 11:41:19
Document(s) archivé(s) le : vendredi 11 juillet 2014 - 12:45:39

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densityofSPDmatrices.pdf
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Emmanuel Chevallier, Augustin Chevallier, Jesus Angulo. Computing Histogram of Tensor Images using Orthogonal Series Density Estimation and Riemannian Metrics. 22nd International Conference on Pattern Recognition (ICPR), 2014, Aug 2014, Stockholm, Sweden. Pattern Recognition (ICPR), 2014 22nd International Conference on. <http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6966883>. <10.1109/ICPR.2014.165>. <hal-00941147v2>

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