In this paper, we present three optimisation techniques, Deterministic Pseudo-Annealing (DPA), Game Strategy Approach (GSA), and Modified Metropolis Dynamics (MMD), in order to carry out image classification using a Markov random field model. For the first approach (DPA), the a posteriori probability of a tentative labelling is generalised to a continuous labelling. The merit function thus defi...

Image Registration is central to different applications such as medical analysis, biomedical systems, image guidance, etc. In this paper we propose a new algorithm for multi-modal image registration. A Bayesian formulation is presented in which a likelihood term is defined using an observation model based on linear intensity transformation functions. The coefficients of these transformations ar...

It was recently shown that there exists a family Z2 Markov random fields which are K but are not isomorphic to Bernoulli shifts [4]. In this paper we show that most distinct members of this family are not isomorphic. This implies that there is a two parameter family of Z2 Markov random fields of the same entropy, no two of which are isomorphic.

The use of random elds, which allows one to take into account the spatial interaction among random variables in complex systems, becomes a frequent tool in numerous problems of statistical mechanics, spatial statistics, neural network modelling, and others. In particular, Markov random eld based techniques can be of exceptional eeciency in some image processing problems, like segmen-tation or e...

Scoring structures of undirected graphical models by means of evaluating the marginal likelihood is very hard. The main reason is the presence of the partition function which is intractable to evaluate, let alone integrate over. We propose to approximate the marginal likelihood by employing two levels of approximation: we assume normality of the posterior (the Laplace approximation) and approxi...

We present a nonparametric Markov Random Field model for classifying texture in images. This model can capture the characteristics of a wide variety of textures, varying from the highly structured to the stochastic. The power of our modelling technique is evident in that only a small training image is required, even when the training texture contains long range characteristics. We show how this...

We propose Markov random fields (MRFs) as a probabilistic mathematical model for unifying approaches to multi-robot coordination or, more specifically, distributed action selection. The MRF model is well-suited to domains in which the joint probability over latent (action) and observed (perceived) variables can be factored into pairwise interactions between these variables. Specifically, these ...

The task of obtaining a line labeling from a greyscale image of trihedral objects presents diiculties not found in the classical line labeling problem. As originally formulated, the line labeling problem assumed that each junction was correctly pre-classiied as being of a particular junction type (e.g. T, Y, arrow); the success of the algorithms proposed have depended critically upon getting th...

Compressive Sensing (CS) combines sampling and compression into a single subNyquist linear measurement process for sparse and compressible signals. In this paper, we extend the theory of CS to include signals that are concisely represented in terms of a graphical model. In particular, we useMarkov Random Fields (MRFs) to represent sparse signals whose nonzero coefficients are clustered. Our new...