We introduce an empirical Bayesian procedure for the simultaneous segmentation of an observed motion field and estimation of the hyper-parameters of a Markov random field prior. The new approach approach exhibits the Bayesian appeal of incorporating prior beliefs, but requires only a qualitative description of the prior, avoiding the requirement for a quantitative specification of its parameter...

a wind farm is a collection of wind turbines built in an area to provide electricity. wind power is a renewable energy resource and an alternative to non-renewable fossil fuels. in this paper impact of wind farms in power system reliability is investigate and a new procedure for reliability assessment of wind farms in hl1 level is proposed. in proposed procedure, application of fuzzy – markov f...

This paper presents a promising super-resolution (SR) approach using maximum a posteriori (MAP) estimation. We consider the high resolution (HR) estimation as a Markov Random Field (MRF), using a transformed gradient field prior to repair the image fuzzy problem caused by MRF. An improved Normalized Convolution method is proposed to obtain a first good estimation. We build a reasonable energy f...

The problem of selecting pair-potentials of finite range for Gibbs random fields is considered as an important step in modelling multi-textured images. In a decision theoretic set-up, the Bayesian procedure is approximated by using Laplace's method for asymptotic expansion of integrals. Certain frequentist properties of the selection procedure are investigated. In particular, its consistency is...

Estimation of the parameters of Markov random field models for spatial and temporal data arises in many applications. There are computational and statistical challenges in developing efficient estimators because of the complexity of the joint distribution of the spatio-temporal models, especially when they involve hidden states that also need to be estimated from the observations. We develop co...

In this paper we propose an novel algorithm for detecting changes in street scenes when the vehicle revisits sections of the street at different times. The proposed algorithm detects structural geometric changes, changes due to dynamically moving objects and as well as changes in the street appearance (e.g. posters put up) between two traversal times. We exploit geometric, appearance and semant...

Markov networks utilize nonembedded probabilistic conditional independencies in order to provide an economical representation of a joint distribution in uncertainty management. In this paper we study several properties of nonembedded conditional independencies and show that they are in fact equivalent. The results presented here not only show the useful characteristics of an important subclass ...

Human perception of image distortions has been widely explored in recent years, however, research has not dealt with distortions due to geometric operations. As a consequence, there is a lack of objective visual quality measures for this class of distortions. In this paper we propose a method of objectively assessing the perceptual quality of geometrically distorted images. Our approach is base...

An efficient estimator for the expectation R f dP is constructed, where P is a Gibbs random field, and f is a local statistic, i. e. a functional depending on a finite number of coordinates. The estimator coincides with the empirical estimator under the conditions stated in Greenwood and Wefelmeyer [6], and covers the known special cases, namely the von Mises statistic for the i.i.d. underlying...

We address the problem of constructing and identifying a valid joint probability density function from a set of specified conditional densities. The approach taken is based on the development of relations between the joint and the conditional densities using Markov random fields (MRFs). We give a necessary and sufficient condition on the support sets of the random variables to allow these relat...