Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Filters, Random Fields and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling
International Journal of Computer Vision
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Markov random field modeling in image analysis
Markov random field modeling in image analysis
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Empirical Study of the Simulation of Various Models used for Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Segmentation Using Causal Markov Random Field Models
Proceedings of the 4th International Conference on Pattern Recognition
Computation of image spatial entropy using quadrilateral Markov random field
IEEE Transactions on Image Processing
A fast nonparametric noncausal MRF-based texture synthesis scheme using a novel FKDE algorithm
IEEE Transactions on Image Processing
Joint random field model for all-weather moving vehicle detection
IEEE Transactions on Image Processing
Stationary Markov random fields on a finite rectangular lattice
IEEE Transactions on Information Theory
Classification of binary random patterns
IEEE Transactions on Information Theory
Bayes Smoothing Algorithms for Segmentation of Binary Images Modeled by Markov Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
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This paper introduces bilateral Markov mesh random field to overcome the shortcomings of the conventional Markov random fields in image modeling. These shortcomings consist of (a) the computational intractability of such fields when expressing the image probability function in the form of the Gibbs distribution function, and (b) the formulation of the image probability function via the product of low-dimensional densities at the expense of obtaining non-symmetrical image models. The properties of bilateral Markov mesh random field are presented and used to derive an image model to address the above shortcomings. As an application, a framework for image restoration is then provided. Restoration results based on this new bilateral Markov mesh random field are compared to the conventional fields to demonstrate its effectiveness.