Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Computational and Applied Mathematics - Random numbers and simulation
Pixel labelling for three-dimensional scenes based on Markov mesh models
Signal Processing
ACM Computing Surveys (CSUR)
Image Segmentation Using Causal Markov Random Field Models
Proceedings of the 4th International Conference on Pattern Recognition
Computational Bayesian Analysis of Hidden Markov Mesh Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
A middle-insertion algorithm for Markov chain simulation of soil layering
Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
Computation of image spatial entropy using quadrilateral Markov random field
IEEE Transactions on Image Processing
Fast computation methods for estimation of image spatial entropy
Journal of Real-Time Image Processing
Bilateral Markov mesh random field and its application to image restoration
Journal of Visual Communication and Image Representation
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Markov random fields are typically used as priors in Bayesian image restoration methods to represent spatial information in the image. Commonly used Markov random fields are not in fact capable of representing the moderate-to-large scale clustering present in naturally occurring images and can also be time consuming to simulate, requiring iterative algorithms which can take hundreds of thousands of sweeps of the image to converge. Markov mesh models, a causal subclass of Markov random fields, are, however, readily simulated. We describe an empirical study of simulated realizations from various models used in the literature, and we introduce some new mesh-type models. We conclude, however, that while large-scale clustering may be represented by such models, strong directional effects are also present for all but very limited parameterizations. It is emphasized that the results do not detract from the use of Markov random fields as representers of local spatial properties, which is their main purpose in the implementation of Bayesian statistical approaches to image analysis. Brief allusion is made to the issue of parameter estimation.