Unexpected spatial patterns in exponential family auto models
Graphical Models and Image Processing
Markov Random Field Texture Models
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
Mean field approximation for PDE-Markov random field models in image analysis
AEE'07 Proceedings of the 6th conference on Applications of electrical engineering
Texture analysis and simulations using Markov random field models
ICS'06 Proceedings of the 10th WSEAS international conference on Systems
Exploratory Point Pattern Analysis for Modeling Biological Data
International Journal of Systems Biology and Biomedical Technologies
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The objective of Bayesian modelling in pattern analysis is aimed to extract the important characteristics of the pattern using a few parameters so as to represent the pattern effectively. The use of Bayesian methods in medical biology and modelling is an approach, which seeks to provide a unified framework within many different image processes. Markov random fields (M.r.f.) modelling are a very popular pattern analysis methods and it plays an important role in pattern recognition and computer vision. In this work, Bayesian models would be presented to illustrate biological phenomena using the Gibbs sampler technique. Finally, methods for estimating model parameters using likelihood techniques are examined, and a model selection procedure is proposed for classifying the neighbourhood structure of the image. The techniques are investigated using simulated and real data from the area of biology.