Computing Histogram of Tensor Images using Orthogonal Series Density Estimation and Riemannian Metrics - Archive ouverte HAL Access content directly
Conference Papers Year :

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

Emmanuel Chevallier
  • Function : Author
  • PersonId : 952014
Augustin Chevallier
  • Function : Author
  • PersonId : 952015
Jesus Angulo

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.
Fichier principal
Vignette du fichier
densityofSPDmatrices.pdf (909.64 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-00941147 , version 1 (07-02-2014)
hal-00941147 , version 2 (11-04-2014)

Identifiers

Cite

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. ⟨10.1109/ICPR.2014.165⟩. ⟨hal-00941147v2⟩
4789 View
305 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More