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...

We present an integrated approach for contour-based grouping and object recognition. Domain knowledge and domainindependent grouping laws are combined in a multi-layered Markov Random Field framework. It provides a basis for propagating top-down knowledge between different processing cues or input modalities. Additionally, the domain dependent MRF-layer can be used in order to evaluate the grou...

This paper presents an optimisation technique to automatically select a set of control parameters for a Markov Random Field. The method is based on the Reactive Tabu Search strategy, and requires to define a suitable fitness function that measures the performance of the MRF algorithm with a given parameters set. The technique is applied to stereo matching thanks to the availability of ground-tr...

A variety of computer vision problems can be optimally posed as Bayesian labeling in which the solution of a problem is de-ned as the maximum a posteriori (MAP) probability estimate of the true labeling. The posterior probability is usually derived from a prior model and a likelihood model. The latter relates to how data is observed and is problem domain dependent. The former depends on how var...

Based on MRF Models Sang-Churl Nam, Masahide Abe and Masayuki Kawamata Department of Electronic Engineering, Graduate School of Engineering, Tohoku University Aoba-yama 6-6-05, Sendai 980-8587, Japan Tel: +81-22-795-7095, Fax: +81-22-263-9169 Abstract: This paper proposes an efficient blotch detection algorithm based on a Markov Random Field (MRF) model with less computational complexity and ...

This paper is concerned with hierarchical Markov Random Field (MRF) models and with their application to sonar image segmentation. We present a novel unsupervised hierarchical MRF model involving a pyramidal label eld and a scale-causal and spatial neighborhood structure. This allows us to more precisely model the local and global characteristics of image content for diierent scales. Such conne...

Image segmentation is a very important technique in image processing. However, it is a very difficult task and there is no single unified approach for all types of images. This paper uses graphical models to design a segmentation algorithm and tests it on some nature images. First, the algorithm over-segments an image into small regions, called superpixels. For each superpixel, we model the pix...

A new criterion for classifying multispectral remote sensing images or textured images by using spectral and spatial information is proposed. The images are modeled with a hierarchical Markov Random Field (MRF) model that consists of the observed intensity process and the hidden class label process. The class labels are estimated according to the maximum a posteriori (MAP) criterion, but some r...

| The association of statistical models and mul-tiresolution data analysis in a consistent and tractable mathematical framework remains an intricate theoretical and practical issue. Several consistent approaches have been proposed recently to combine Markov Random Field (MRF) models and multiresolution algorithms in image analysis: renormalization group, subsampling of stochastic processes, MRF...

This paper proposes a novel statistical approach to intelligent document retrieval. It seeks to offer a more structured and extensible mathematical approach to the term generalization done in the popular Latent Semantic Analysis (LSA) approach to document indexing. A Markov Random Field (MRF) is presented that captures relationships between terms and documents as probabilistic dependence assump...