In this paper, we present the region based hidden Markov random field model (RBHMRF), which encodes the characteristics of different brain regions into a probabilistic framework for brain MR image segmentation. The recently proposed TV+L model is used for region extraction. By utilizing different spatial characteristics in different brain regions, the RMHMRF model performs beyond the current st...

In this work, we propose a novel nonparametric Bayesian method for clustering of data with spatial interdependencies. Specifically, we devise a novel normalized Gamma process, regulated by a simplified (pointwise) Markov random field (Gibbsian) distribution with a countably infinite number of states. As a result of its construction, the proposed model allows for introducing spatial dependencies...

Pathology image analysis is an essential procedure for clinical diagnosis of many diseases. To boost the accuracy and objectivity detection, nowadays, increasing number computer-aided (CAD) system proposed. Among these methods, random field models play indispensable role in improving performance. In this review, we present a comprehensive overview pathology based on markov fields (MRFs) conditi...

A method is proposed for obtaining the local clique set from a neighbourhood system. The Markov random field model, which is used extensively in image processing, is defined with respect to a neighbourhood system. The mathematical interpretation of the model is defined with respect to the corresponding clique set. We present a systematic method for extracting the complete local clique set from ...

{ Satellite images contain an enormous amount of spatial information. To capture that information we propose, in the framework of a stochastic modelling of the image, the use of Gibbs Markov random elds. We expand on a particular model suitable for the use with typical remote sensing images. We demonstrate the capabilities of that model with two examples. In particular, we perform directed quer...

of the Dissertation . . . . . . . . . . . . . . . . . . . . . . . . . xvi

Structured prediction models have been found to learn effectively from a few large examples— sometimes even just one. Despite empirical evidence, canonical learning theory cannot guarantee generalization in this setting because the error bounds decrease as a function of the number of examples. We therefore propose new PAC-Bayesian generalization bounds for structured prediction that decrease as...

Determining the semantic intent of web queries not only involves identifying their semantic class, which is a primary focus of previous works, but also understanding their semantic structure. In this work, we formally define the semantic structure of noun phrase queries as comprised of intent heads and intent modifiers. We present methods that automatically identify these constituents as well a...

The incidence function approach to modelling of metapopulation dynamics is critically examined both from the biological perspective and for technical issues, the latter by placing the model in the context of Markov random fields and the statistical analysis of binary lattice systems. The claim that the model can be used to estimate time-process parameters from spatialpattern data is examined us...