Effective abnormality detection and diagnosis in Magnetic Resonance Images (MRIs) requires a robust segmentation strategy. Since manual segmentation is a time-consuming task which engages valuable human resources, automatic MRI segmentations received an enormous amount of attention. For this goal, various techniques have been applied. However, Markov Random Field (MRF) based algorithms have pro...

This paper proposes a stochastic approach to estimate the disparity field combined with line field. In the maximum a posteriori (MAP) method based on Markov random field (MRF) model, it is important to optimize and converge the Gibbs potential function corresponding to the perturbed disparity field. The proposed optimization method, stochastic diffusion, takes advantage of the probabilistic dis...

A contamination source identification problem in constant porous media flow is addressed by solving the advection-dispersion equation (ADE) with a hierarchical Bayesian computation method backward through time. The contaminant concentration is modeled as a pair-wise Markov Random Field (MRF) and the distribution is updated using current concentration measurements at finite locations. Hierarchic...

This paper describes an online handwritten English cursive word recognition method using a segmentation-free Markov random field (MRF) model in combination with an offline recognition method which uses pseudo 2D bi-moment normalization (P2DBMN) and modified quadratic discriminant function (MQDF). It extracts feature points along the pen-tip trace from pen-down to pen-up and uses the feature poi...

-ln this paper, we propose a Markov Random Field model-based approach as a unified and systematic way for modeling, encoding and applying scene knowledge to the image understanding problem. In our proposed scheme we formulate the image segmentation and interpretation problem as an integrated scheme and solve it through a general optimization algorithm. More specifically, the image is first segm...

To enable content based functionalities in video processing algorithms, decomposition of scenes into semantic objects is necessary. A semi-automatic Markov random field based multiresolution algorithm is presented for video object extraction in a complex scene. In the first frame, spatial segmentation and user intervention determine objects of interest. The specified objects are subsequently tr...

For many years, vehicle tracking in traffic images has suffered from the problems of occlusions and sudden variations in illumination. In order to resolve these occlusion problems, we have been proposing the Spatio-Temporal Markov Random Field model(S-T MRF) for segmentation of spatio-temporal images. This S-T MRF optimizes the segmentation boundaries of occluded vehicles and their motion vecto...

In this paper we present a system for recognition of handwritten words on literal check amounts which advantageously combines HMMs and Markov random fields (MRFs). It operates, in a holistic manner, at pixel level on height normalised word images which are viewed as random field realizations. The HMM analyses the image along the horizontal writing direction, in a specific state observation prob...

We present a novel variational formulation of discrete deformable registration as the minimization of a convex energy functional that involves diffusion regularization. We show that a finite difference solution (FD) of the variational formulation is equivalent to a continuous-valued Gaussian Markov random field (MRF) energy minimization formulation previously proposed as the random walker defor...

Acknowledgments I would like to thank my advisor, Pradeep Ravikumar, for inspiration, guidance, and encouragement on this work. In addition, I would like to thank Ali Jalali for his collaboration and work on the proof techniques and theoretical analysis used in this paper. Also, I would also like to thank Inderjit Dhillon and the students of his lab for motivation and many stimulating conversat...