Reviews a significant component of the rich field of statistical multiresolution (MR) modeling
and processing. These MR methods have found application and permeated the literature of …

The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …

Publisher Summary This chapter presents an overview of dynamic Bayesian models.
Dynamic Bayesian modelling and forecasting of time series is one of the most important …

In this paper, we propose a probabilistic model for detecting relevant changes in registered
aerial image pairs taken with the time differences of several years and in different seasonal …

We propose a Markov random field (MRF) image segmentation model, which aims at
combining color and texture features. The theoretical framework relies on Bayesian …

Due to the enormous quantity of radar images acquired by satellites and through shuttle
missions, there is an evident need for efficient automatic analysis tools. This paper describes …

Markov random fields (MRFs) provide a useful and theoretically well-established tool for
integrating temporal contextual information into the classification process. In particular, when …

The deep self-organizing map (DSOM) was introduced to embed hierarchical feature
abstraction capability to self-organizing maps (SOMs). This paper presents an extended …

Hidden Markov fields (HMF) are widely used in image processing. In such models, the
hidden random field of interest X=(Xs) is a Markov field, and the distribution p (y/x) of the …

The aim of this paper is to apply the recent pairwise Markov chain model, which generalizes
the hidden Markov chain one, to the unsupervised restoration of hidden data. The main …