We propose a method for retrieving a piecewise smooth surface from noisy data. In data acquired by a scanning process sampled points are almost never on the discontinuities making reconstruction of surfaces with sharp features difficult. Our method is based on a Markov Random Field (MRF) formulation of a surface prior, with the surface represented as a collection of small planar patches, the su...

In this paper, we propose a Markov random field (MRF) image segmentation model which aims at combining color and texture features. The theoretical framework relies on Bayesian estimation associated with combinatorial optimization (Simulated Annealing). The segmentation is obtained by classifying the pixels into different pixel classes. These classes are represented by multi-variate Gaussian dis...

Markov Random Field (MRF) approaches have been widely studied for Synthetic Aperture Radar (SAR) image segmentation, but they have a large computational cost and hence are not widely used in practice. Fortunately parallel algorithms have been documented to enjoy significant speedups when ported to run on a graphics processing units (GPUs) instead of a standard CPU. Presented here is an implemen...

This work deals with unsupervised sonar image segmenta-tion. We present a new estimation segmentation procedure using the recent iterative method of estimation called Iterative Conditional Estimation (ICE) 1]. This method takes into account the variety of the laws in the distribution mixture of a sonar image and the estimation of the parameters of the label eld (modeled by a Markov Random Field...

The purpose of this paper it to explore the relationship between the rate-distortion characteristics of multiscale binary shape and Markov random field (MRF) parameters. For coding, it is important that the input parameters that will be used to define this relationship be able to distinguish between the same shape at different scales, as well as different shapes at the same scale. We consider a...

This work deals with hyperspectral image analysis in the absence of ground-truth. The method adopts a projection pursuit (PP) procedure with entropy index to reduce the dimensionality followed by Markov Random Field (MRF) model based segmentation. Ordinal optimization approach to PP determines a set of “ good enough projections” with high probability the best among which is chosen with the help...

Problem of impulse noise reduction is a very well studied problem in image processing community and many different approaches have been proposed to tackle this problem. In the current work, the problem of fixed value impulse noise (salt and pepper) removal from images is investigated by use of a Markov Random Field (MRF) models with smoothness priors. After the formulation of the problem as an ...

In recent years, multi-atlas fusion methods have gained significant attention in medical image segmentation. In this paper, we propose a general Markov Random Field (MRF) based framework that can perform edge-preserving smoothing of the labels at the time of fusing the labels itself. More specifically, we formulate the label fusion problem with MRF-based neighborhood priors, as an energy minimi...

In order to provide precision and objective image quality measures (IQMs) for the low dose CT (Computed Tomography) images, various general IQMs need to be validated and analyzed. The IQM based on Markov Random Field (MRF) has not been check and validated by a comprehensive distorted database. First choose a standard distorted image database of LIVE (Laboratory for Image & Video Engineering) to...

Recent studies in image denoising show the importance of using (i) multiresolution transformations with improved orientation selectivity and (ii) the appropriate spatial context models. Regarding the first point, a number of the so-called " geometrical representations " have been introduced that represent image discontinuities better than the classical wavelets, which also results in better noi...