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Copulas in vectorial Hidden Markov chains for multicomponent image segmentation

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Parametric estimation of non-Gaussian multidimensional probability density function (pdf) is a difficult problem that is required by many applications in signal and image processing. A lot of efforts has been devoted to methods from multivariate analysis such as Principal or Independent Component Analysis (PCA and ICA). In this work, we introduce an alternative solution based on a very general class of multivariate models called 'copulas'. Useful copulas models for image classification are used in the frame of multidimensional mixture estimation arising in the segmentation of multicomponent images, when using a vectorial Hidden Markov Chain (HMC).

Original languageEnglish
Title of host publication2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05 - Proceedings - Image and Multidimensional Signal Processing Multimedia Signal Processing
PagesII717-II720
DOIs
Publication statusPublished - 1 Dec 2005
Event2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05 - Philadelphia, PA, United States
Duration: 18 Mar 200523 Mar 2005

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
VolumeII
ISSN (Print)1520-6149

Conference

Conference2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05
Country/TerritoryUnited States
CityPhiladelphia, PA
Period18/03/0523/03/05

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