In computer vision, many applications have been formulated as Markov Random Field (MRF) optimization or energy minimization problems. To solve them effectively, numerous algorithms have been developed, including the deterministic and stochastic sampling algorithms. The deterministic algorithms include Graph Cuts, Belief Propagation, and Tree-Reweighted Message Passing while the stochastic sampl...

Using a probabilistic interpretation of an n dimensional extension of Papoulis's Generalized Sampling Theorem, an iterative algorithm has been devised for 3D reconstruction of a Lambertian surface at subpixel accuracy. The problem has been formulated as an optimization one in a Bayesian framework. The latter allows for introducing a priori information on the solution, using Markov Random Fields...

Understanding the heterogeneity over spatial locations is an important problem that has been widely studied in many applications such as economics and environmental science. In this paper, we focus on regression models for panel data analysis, where repeated measurements are collected time at various locations. We propose a novel class of nonparametric priors combines Markov random field (MRF) ...

In minimally invasive surgery, dense 3D surface reconstruction is important for surgical navigation and integrating pre- and intra-operative data. Despite recent developments in 3D tissue deformation techniques, their general applicability is limited by specific constraints and underlying assumptions. The need for accurate and robust tissue deformation recovery has motivated research into fusin...

Our goal is to produce the best reconstructions of an image given a noisy input image I0. We write any possible reconstruction of the image as a random vector I of pixel values. The best reconstruction is the one that maximizes the posterior probability p(I|I0) = p(I0|I) p(I) (1) This posterior probability is constructed as a Markov Random Field (MRF). More specifically, the random variable I =...

We investigate Discriminative Random Fields (DRF) which provide a principled approach for combining local discriminative classifiers that allow the use of arbitrary overlapping features, with adaptive data-dependent smoothing over the label field. We discuss the differences between a traditional Markov Random Field (MRF) formulation and the DRF model, and compare the performance of the two mode...

Markov random field (MRF) models, including conditional random field models, are popular in computer vision. However, in order to be computationally tractable, they are limited to incorporating only local interactions and cannot model global properties such as connectedness, which is a potentially useful high-level prior for object segmentation. In this work, we overcome this limitation by deri...

In this paper, we analyze Markov Random Field (MRF) as a spatial regularizer in fMRI detection. The low signal-to-noise ratio (SNR) in fMRI images presents a serious challenge for detection algorithms, making regularization necessary to achieve good detection accuracy. Gaussian smoothing, traditionally employed to boost SNR, often produces over-smoothed activation maps. Recently, the use of MRF...

In this paper we propose an extension to the standard Markov Random Field (MRF) model in order to handle layers. Our extension, which we call a Factorial MRF (FMRF), is analogous to the extension from Hidden Markov Models (HMM’s) to Factorial HMM’s. We present an efficient EM-based algorithm for inference on Factorial MRF’s. Our algorithm makes use of the fact that layers are a priori independe...

Several segmentation techniques were evaluated for their effectiveness in distinguishing lesion from background in dermatoscopic images of pigmented lesions (moles and melanomas). These included 5 techniques previously used for segmentation of pigmented lesions, and several new techniques based on stabilized inverse diffusion equations (SIDE) and Markov random fields (MRF). Novel multiresolutio...