Copulas in vectorial hidden Markov chains for multicomponent image segmentation

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 effort 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).