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Mrf parameter estimation by mcmc method (1999).
| Content Provider | CiteSeerX |
|---|---|
| Author | Wang, Lei Liu, Jun Li, Stan Z. |
| Abstract | Markov random fields (MRFs) have been used in image restoration, texture classification and segmentation as a prior model of pattern distributions. An important issue in MRF modeling is MRF parameter estimation. In this paper, an MRF parameter estimation method is proposed for effective and efficient estimation of MRF parameters. The method is based on Markov Chain Monte Carlo (MCMC) in which a Markov chain is constructed to sample the MRF parameters using Monte Carlo method. Experiments show that the proposed method is more accurate than the least squares fit method, and can be used for effective estimation of multi-graylevel texture parameters. This method can be extended for the use in multiresolution analysis of texture modeling and segmentation of textured images. Key Words: MRF, MCMC, Least Squares Fit, Parameter Estimation, Pseudolikelihood 1 Introduction Mathematical modeling in pattern analysis is aimed to extract the intrinsic characteristics of the pattern in a few paramete... |
| File Format | |
| Publisher Date | 1999-01-01 |
| Access Restriction | Open |
| Subject Keyword | Mrf Parameter Estimation Mcmc Method Mrf Parameter Markov Random Field Parameter Estimation Efficient Estimation Prior Model Multiresolution Analysis Intrinsic Characteristic Mrf Parameter Estimation Method Markov Chain Texture Modeling Pattern Analysis Markov Chain Monte Carlo Textured Image Introduction Mathematical Modeling Pattern Distribution Monte Carlo Method Texture Classification Effective Estimation Square Fit Method Least Square Fit Important Issue Key Word Multi-graylevel Texture Parameter Mrf Modeling Image Restoration |
| Content Type | Text |
