Double Markov random fields and Bayesian image segmentation

Double Markov random fields and Bayesian image segmentation

Markov random fields are used extensively in modelbased approaches to image segmentation and, under the Bayesian paradigm, are implemented through Markov chain Monte Carlo (MCMC) methods. In this paper, we describe a class of such models (the double Markov random field) for images composed of several textures, which we consider to be the natural hierarchical model for such a task. We show how s...

Markov Random Fields in Image Segmentation

Bayesian image classification using Markov random fields

In this paper, we present three optimisation techniques, Deterministic Pseudo-Annealing (DPA), Game Strategy Approach (GSA), and Modified Metropolis Dynamics (MMD), in order to carry out image classification using a Markov random field model. For the first approach (DPA), the a posteriori probability of a tentative labelling is generalised to a continuous labelling. The merit function thus defi...

Cluster-Based Image Segmentation Using Fuzzy Markov Random Field

Image segmentation is an important task in image processing and computer vision which attract many researchers attention. There are a couple of information sets pixels in an image: statistical and structural information which refer to the feature value of pixel data and local correlation of pixel data, respectively. Markov random field (MRF) is a tool for modeling statistical and structural inf...

Adaptive color image segmentation using Markov random fields

A new framework for color image segmentation is introduced generalizing the concepts of point-based and spatially-based methods. This framework is based on Markov Random Fields using a Continuous Gibbs Sampler. The Markov Random Fields approach allows for a rigorous computational framework where local and global spatial constraints can be globally optimized. Using a Continuous Gibbs Sampler ena...