This paper deals with a recent statistical model based on fuzzy Markov random chains for image segmentation, in the context of stationary and non-stationary data. On one hand, fuzzy scheme takes into account discrete and continuous classes through the modeling of hidden data imprecision and on the other hand, Markovian Bayesian scheme models the uncertainty on the observed data. A non-stationar...

Estimation of the extent and spread of wildland fires is an important application of high spatial resolution multispectral images. This work addresses a fuzzy segmentation algorithm to map fire extent, active fire front, hot burn scar, and smoke regions based on a statistical model. The fuzzy results are useful data sources for integrated fire behavior and propagation models built using Dynamic...

Methods are investigated in order to partition in k groups a set of n multivariate observation vectors located at neighboring geographic sites; applications include image segmentation, ecological or soil data cartography. In this perspective, the deterministic variant of the EM procedure described in Zhang (1992) for hidden Markov random fields is shown to be equivalent to the optimization of a...

We present a new brain segmentation framework which we apply to T1-weighted magnetic resonance image segmentation. The innovation of the algorithm in comparison to the state-of-the-art of nonsupervised brain segmentation is twofold. First, the algorithm is entirely non-parametric and non-supervised. We can therefore enhance the classically used gray level information of the images by other feat...