Markov random field

About: Markov random field is a research topic. Over the lifetime, 5669 publications have been published within this topic receiving 179568 citations. The topic is also known as: MRF.

Log Gaussian Cox processes and spatially aggregated disease incidence data.

Abstract: This article presents a methodology for modeling aggregated disease incidence data with the spatially continuous log-Gaussian Cox process. Statistical models for spatially aggregated disease incidence data usually assign the same relative risk to all individuals in the same reporting region (census areas or postal regions). A further assumption that the relative risks in two regions are independent given their neighbor's risks (the Markov assumption) makes the commonly used Besag-York-Mollie model computationally simple. The continuous model proposed here uses a data augmentation step to sample from the posterior distribution of the exact locations at each step of an Markov chain Monte Carlo algorithm, and models the exact locations with an log-Gaussian Cox process. A simulation study shows the log-Gaussian Cox process model consistently outperforming the Besag-York-Mollie model. The method is illustrated by making inference on the spatial distribution of syphilis risk in North Carolina. The effect of several known social risk factors are estimated, and areas with risk well in excess of that expected given these risk factors are identified.

Single-image super-resolution based on Markov random field and contourlet transform

Abstract: Learning-based methods are well adopted in image super-resolution. In this paper, we propose a new learning-based approach using contourlet transform and Markov random field. The proposed algorithm employs contourlet transform rather than the conventional wavelet to represent image features and takes into account the correlation between adjacent pixels or image patches through the Markov random field (MRF) model. The input low-resolution (LR) image is decomposed with the contourlet transform and fed to the MRF model together with the contourlet transform coefficients from the low- and high-resolution image pairs in the training set. The unknown high-frequency components/coefficients for the input low-resolution image are inferred by a belief propagation algorithm. Finally, the inverse contourlet transform converts the LR input and the inferred high-frequency coefficients into the super-resolved image. The effectiveness of the proposed method is demonstrated with the experiments on facial, vehicle plate, and real scene images. A better visual quality is achieved in terms of peak signal to noise ratio and the image structural similarity measurement.

Weighted Joint Bilateral Filter with Slope Depth Compensation Filter for Depth Map Refinement

Abstract: In this paper, we propose a new refinement filter for depth maps. The filter convolutes a depth map by a jointly computed kernel on a natural image with a weight map. We call the filter weighted joint bilateral filter. The filter fits an outline of an object in the depth map to the outline of the object in the natural image, and it reduces noises. An additional filter of slope depth compensation filter removes blur across object boundary. The filter set’s computational cost is low and is independent of depth ranges. Thus we can refine depth maps to generate accurate depth map with lower cost. In addition, we can apply the filters for various types of depth map, such as computed by simple block matching, Markov random field based optimization, and Depth sensors. Experimental results show that the proposed filter has the best performance of improvement of depth map accuracy, and the proposed filter can perform real-time refinement.

A statistical approach to SENSE regularization with arbitrary k-space trajectories.

Abstract: SENSE reconstruction suffers from an ill-conditioning problem, which increasingly lowers the signal-to-noise ratio (SNR) as the reduction factor increases. Ill-conditioning also degrades the convergence behavior of iterative conjugate gradient reconstructions for arbitrary trajectories. Regularization techniques are often used to alleviate the ill-conditioning problem. Based on maximum a posteriori statistical estimation with a Huber Markov random field prior, this study presents a new method for adaptive regularization using the image and noise statistics. The adaptive Huber regularization addresses the blurry edges in Tikhonov regularization and the blocky effects in total variation (TV) regularization. Phantom and in vivo experiments demonstrate improved image quality and convergence speed over both the unregularized conjugate gradient method and Tikhonov regularization method, at no increase in total computation time. Magn Reson Med 60:414 – 421, 2008. © 2008 Wiley

Recognizing activities with multiple cues

Abstract: In this paper, we introduce a first-order probabilistic model that combines multiple cues to classify human activities from video data accurately and robustly. Our system works in a realistic office setting with background clutter, natural illumination, different people, and partial occlusion. The model we present is compact, requires only fifteen sentences of first-order logic grouped as a Dynamic Markov Logic Network (DMLNs) to implement the probabilistic model and leverages existing state-of-the-art work in pose detection and object recognition.