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...

In this paper, we propose a novel action recognition framework. The method uses pictorial structures and shrinkage optimized directed information assessment (SODA) coupled with Markov Random Fields called SODA+MRF to model the directional temporal dependency and bidirectional spatial dependency. As a variant of mutual information, directional information captures the directional information flo...

This paper presents a novel image segmentation algorithm, which uses a biologically inspired paradigm known as swarm intelligence to segment images. A more efficient MRF based clustering algorithm that incorporated the Markov Random Field (MRF) theory and the Quantum Particle Swarm Optimization (QPSO) algorithm is proposed. the QPSO algorithm is ised to optimize the energy function which is a c...

We presented and evaluated a new Bayesian method for range image segmentation. The method proceeds in to stages. First, an initial segmentation was produced by a randomized region growing technique. The produced segmentation was considered as a degraded version of the ideal segmentation, which should be then refined. In the second stage, image pixels not labeled in the first stage were labeled ...

Herein, we propose a novel multi-layer Markov random field (MRF) image segmentation model which aims at combining color and texture features: Each feature is associated to a so called feature layer, where an MRF model is defined using only the corresponding feature. A special layer is assigned to the combined MRF model. This layer interacts with each feature layer and provides the segmentation ...

A novel image deblurring method based on high-order non-local range Markov Random Field (NLR-MRF) prior is proposed in the paper. NLR-MRF provides an effective framework to model the statistical prior of natural images and leads to excellent performance in the application of image denoising and inpainting. Moreover, the framework will be extended to image deblurring in our work. Instead of comm...

Background modeling is important for video surveillance. In the paper, we present a novel background modeling algorithm based on probabilistic graphic model and Gibbs Sampling. The background is modeled at different resolution level by image pyramids. We develop a time dependent pyramidal Markov Random Field (MRF) to represent the state of foreground/background at each pixel in the pyramid. Bot...

A novel approach to obtain precise segmentation of synthetic aperture radar (SAR) images using Markov random field model on region adjacency graph (MRF-RAG) is presented. First, to form a RAG, the watershed algorithm is employed to obtain an initially over-segmented image. Then, a novel MRF is defined over the RAG instead of pixels so that the erroneous segmentation caused by speckle in SAR ima...

Herein, we propose a new Markov random field (MRF) image segmentation model which aims at combining color and texture features. The model has a multi-layer structure: Each feature has its own layer, called feature layer, where an MRF model is defined using only the corresponding feature. A special layer is assigned to the combined MRF model. This layer interacts with each feature layer and prov...

A novel Markov random field (MRF) model is proposed for roof-edge (as well as step-edge) preserving image smoothing. Image surfaces containing roof-edges are represented by piecewise continuous polynomial functions governed by a few parameters. Piecewise smoothness constraint is imposed on these parameters rather than on the surface heights as is in traditional models for step-edges. In this wa...