For any stationary Zd Gibbs measure that satisfies strong spatial mixing, we obtain sequences of upper and lower approximations that converge to its entropy. In the case d = 2, these approximations are efficient in the sense that they are accurate to within ε and can be computed in time polynomial in 1/ε.

In this paper we address the problem of the identification of text from noisy documents. We segment and identify handwriting from machine printed text because 1) handwriting in a document often indicates corrections, additions or other supplemental information that should be treated differently from the main or body content, and 2) the segmentation and recognition techniques for machine printed...

We consider the problem of inferring the underlying graph using samples from a Markov random field defined on the graph. In particular, we consider the special but interesting case when the underlying graph comes from a distribution on sparse graphs. We provide matching upper and lower bounds for the sample-complexity of learning the underlying graph of a hard-core model, when the underlying gr...

Most approaches to topic modeling assume an independence between documents that is frequently violated. We present an topic model that makes use of one or more user-specified graphs describing relationships between documents. These graph are encoded in the form of a Markov random field over topics and serve to encourage related documents to have similar topic structures. Experiments on show upw...

In this paper, the problem of the detection of road networks in high resolution Synthetic Aperture Radar (SAR) images is addressed. Our method, which is an improvement of previous work based on line extraction and connection with Markov random field, is dedicated to dense urban areas. The major modifications are, first, the introduction of a classification in order to improve both the level of ...

Probabilistic graphical models for continuous variables can be built out of either parametric or nonparametric conditional density estimators. While several research efforts have been focusing on parametric approaches (such as Gaussian models), kernel-based estimators are still the only viable and wellunderstood option for nonparametric density estimation. This paper develops a semiparametric e...

This paper deals with segmentation of breast anatomical regions, pectoral muscle, fatty and fibroglandular regions, using a Bayesian approach. This work is a part of a computer aided diagnosis project aiming at evaluating breast cancer risk and its association with characteristics (density, texture, etc.) of regions of interest on digitized mammograms. Novelty in this paper consists in applying...

In this project1, we first study the Gaussian-based hidden Markov random field (HMRF) model and its expectationmaximization (EM) algorithm. Then we generalize it to Gaussian mixture model-based hidden Markov random field. The algorithm is implemented in MATLAB. We also apply this algorithm to color image segmentation problems and 3D volume segmentation problems.