MRF parameter estimation by MCMC method
نویسندگان
چکیده
Markov random "eld (MRF) modeling is a popular pattern analysis method and MRF parameter estimation plays an important role in MRF modeling. In this paper, a method based on Markov Chain Monte Carlo (MCMC) is proposed to estimate MRF parameters. Pseudo-likelihood is used to represent likelihood function and it gives a good estimation result. ( 2000 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
منابع مشابه
Estimation Methods for One-Parameter Testlet Models
This study demonstrated the equivalence between the Rasch testlet model and the three-level one-parameter testlet model and explored the Markov Chain Monte Carlo (MCMC) method for model parameter estimation in WINBUGS. The estimation accuracy from the MCMC method was compared with those from the marginalized maximum likelihood estimation (MMLE) with the expectation-maximization algorithm in Con...
متن کاملML parameter estimation for Markov random fields with applications to Bayesian tomography
Markov random fields (MRF's) have been widely used to model images in Bayesian frameworks for image reconstruction and restoration. Typically, these MRF models have parameters that allow the prior model to be adjusted for best performance. However, optimal estimation of these parameters(sometimes referred to as hyper parameters) is difficult in practice for two reasons: i) direct parameter esti...
متن کاملInterpolation Techniques for Mcmc Parameter Estimation on Compact Binary Coalescence Gravitational-wave Signals
With gravitational-wave detection on the horizon, astronomers look for ways of extracting useful information from a detected gravitational wave. Like its electromagnetic cousin, a gravitational wave carries important information about the characteristics of its source, and these characteristics can be recovered through numerical analysis. Using one promising technique known as a Metropolis-Hast...
متن کاملJoint Parameter Estimation and Restoration using MRF Models and Homotopy Continuation Method
This paper presents a joint strategy for parameter estimation of Markov Random Field (MRF) model and image restoration. The proposed scheme is an unsupervised one in the sense that no a priori knowledge of the actual image is assumed. The technique of homotopy continuation method is employed to estimate the model parameters. The model considered involves line fields and is tested on real images...
متن کاملA Homotopy Continuation Method for Parameter Estimation in MRF Models and Image Restoration
In this paper, we present an alternate approach to estimate the parameters of a Markov random field (MRF) model for images using the concepts of homotopy continuation method. We also develop a joint parameter estimation and image restoration scheme where we have used a fairly general model involving the line fields and tested on a real image. Simulation results using gray level images are prese...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Pattern Recognition
دوره 33 شماره
صفحات -
تاریخ انتشار 2000