Unsupervised multiband image segmentation using hidden Markov quadtree and copulas

This paper deals with hidden Markov quadtree model for multiband image segmentation. This task, requiring multivariate probability density computations for the data likelihood term, is often confronted with the lack of analytical multidimensional expressions in the non-gaussian case. Thus, multidimensional Gaussian distribution is usually used for its simplicity, even if Gaussian assumption is not always verified. In this work, we propose a new approach based on copula theory to compute multivariate density on Markov quadtree.