Maximum A-Posteriori Estimation of Random Fields - Elliptic Gaussian Fields Observed via a Noisy Channel

Bayesian Parameter Estimation and Segmentation in the Multi-Atlas Random Orbit Model

This paper examines the multiple atlas random diffeomorphic orbit model in Computational Anatomy (CA) for parameter estimation and segmentation of subcortical and ventricular neuroanatomy in magnetic resonance imagery. We assume that there exist multiple magnetic resonance image (MRI) atlases, each atlas containing a collection of locally-defined charts in the brain generated via manual delinea...

Efficient Spatial Prediction Using Gaussian Markov Random Fields under Uncertain Localization

In this paper, we develop efficient spatial prediction algorithms using Gaussian Markov random fields (GMRFs) under uncertain localization and sequential observations. We first review a GMRF as a discretized Gaussian process (GP) on a lattice, and justify the usage of maximum a posteriori (MAP) estimates of noisy sampling positions in making inferences. We show that the proposed approximation c...

Maximum Lilkelihood and Restricted Maximum Likelihood Estimation for a Class of Gaussian Markov Random Fields Maximum Likelihood and Restricted Maximum Likelihood Estimation for a Class of Gaussian Markov Random Fields

IMAGE SEGMENTATION USING GAUSSIAN MIXTURE MODEL

  Stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. In this paper, we have learned Gaussian mixture model to the pixels of an image. The parameters of the model have estimated by EM-algorithm.   In addition pixel labeling corresponded to each pixel of true image is made by Bayes rule. In fact, ...

Improving Multispectral Image Classification by Using Maximum Pseudo-Likelihood Estimation and Higher-Order Markov Random Fields

In this paper we address the multispectral image contextual classification problem following a Maximum a Posteriori (MAP) approach. The classification model is based on a Bayesian paradigm, with the definition of a Gaussian Markov Random Field model (GMRF) for the observed data and a Potts model for the a priori knowledge. The MAP estimator is approximated by the Game Strategy Approach (GSA) al...