In this paper we present Discriminative Random Fields (DRF), a discriminative framework for the classification of natural image regions by incorporating neighborhood spatial dependencies in the labels as well as the observed data. The proposed model exploits local discriminative models and allows to relax the assumption of conditional independence of the observed data given the labels, commonly...

This paper deals with the classification of color video sequences using Markov Random Fields (MRF) taking into account motion information. The theoretical framework relies on Bayesian estimation associated with MRF modelization and combinatorial optimization (Simulated Annealing). In the MRF model, we use the CIE-luv color metric because it is close to human perception when computing color diff...

In this paper, color image segmentation problem is cast as a pixel labeling problem in stochastic framework. The observed color image is assumed to be the degraded version of the image pixel label process. RGB color model is employed to model the color. A new Double Markov Random Field (DMRF) model is proposed to model the intraplane label process and also the interplane label process. The pixe...

In this paper we present a new method for spatial regularization of functional connectivity maps based on Markov Random Field (MRF) priors. The high level of noise in fMRI leads to errors in functional connectivity detection algorithms. A common approach to mitigate the effects of noise is to apply spatial Gaussian smoothing, which can lead to blurring of regions beyond their actual boundaries ...

The problem that the Markov random field (MRF) model captures the structural as well as the stochastic textures for remote sensing image segmentation is considered. As the one-point clique, namely, the external field, reflects the priori knowledge of the relative likelihood of the different region types which is often unknown, one would like to consider only two-pairwise clique in the texture. ...

A modified version of MRFFCM (Markov Random Field Fuzzy C means) based SAR (Synthetic aperture Radar) image change detection method is proposed in this paper. It involves three steps: Difference Image (DI) generation by using Gauss-log ratio operator, speckle noise reduction by SRAD (Speckle Reducing Anisotropic Diffusion), and the detection of changed regions by using MRFFCM. The proposed meth...

This paper addresses the problem of lip segmentation in color space that is a crucial issue to a successful lip-reading system. We present a new segmentation approach to lip contour extraction by taking account of the maximum a posterior Markov random field (MAPMRF) framework. We first examine various color models and select a simple color transform derived from LUX and 1976 CIELAB color space ...

1 Markov Random Field Model The following section will outline the mathematical model in our contact prediction method that is essentially identical to the plmDCA (Ekeberg et al., 2013) and GREMLIN (Kamisetty et al., 2013) methods. We eliminate transitive interactions in the observed interaction network by learning a generative model of the MSA using a Markov Random Field (MRF). Assuming we hav...

UAVs equipped with high-resolution thermal cameras provide an excellent investigative tool used for a multitude of building-specific applications, including roof insulation inspection. We have presented in this study a relative thermographic calibration algorithm and a superpixel Markov Random Field model to address problems in thermal infrared inspection of roof insulation using UAVs. The rela...

3D Mesh segmentation has become an important research field in computer graphics during the past decades. Many geometry based and semantic oriented approaches for 3D mesh segmentation has been presented. In this paper, we present a definition of mesh segmentation according to labeling problem. Inspired by the Markov Random Field (MRF) based image segmentation, we propose a new framework of 3D m...