Markov random field

About: Markov random field is a research topic. Over the lifetime, 5669 publications have been published within this topic receiving 179568 citations. The topic is also known as: MRF.

Multilayer Markov Random Field Models for Change Detection in Optical Remote Sensing Images

Abstract: In this paper, we give a comparative study on three Multilayer Markov Random Field (MRF) based solutions proposed for change detection in optical remote sensing images, called Multicue MRF, Conditional Mixed Markov model, and Fusion MRF. Our purposes are twofold. On one hand, we highlight the significance of the focused model family and we set them against various state-of-the-art approaches through a thematic analysis and quantitative tests. We discuss the advantages and drawbacks of class comparison vs. direct approaches, usage of training data, various targeted application fields and different ways of ground truth generation, meantime informing the Reader in which roles the Multilayer MRFs can be efficiently applied. On the other hand we also emphasize the differences between the three focused models at various levels, considering the model structures, feature extraction, layer interpretation, change concept definition, parameter tuning and performance. We provide qualitative and quantitative comparison results using principally a publicly available change detection database which contains aerial image pairs and Ground Truth change masks. We conclude that the discussed models are competitive against alternative state-of-the-art solutions, if one uses them as pre-processing filters in multitemporal optical image analysis. In addition, they cover together a large range of applications, considering the different usage options of the three approaches.

Bayesian grid matching

Abstract: A method for locating distorted grid structures in images is presented. The method is based on the theories of template matching and Bayesian image restoration. The grid is modeled as a deformable template. Prior knowledge of the grid is described through a Markov random field (MRF) model which represents the spatial coordinates of the grid nodes. Knowledge of how grid nodes are depicted in the observed image is described through the observation model. The prior consists of a node prior and an arc (edge) prior, both modeled as Gaussian MRFs. The node prior models variations in the positions of grid nodes and the arc prior models variations in row and column spacing across the grid. Grid matching is done by placing an initial rough grid over the image and applying an ensemble annealing scheme to maximize the posterior distribution of the grid. The method can be applied to noisy images with missing grid nodes and grid-node artifacts and the method accommodates a wide range of grid distortions including: large-scale warping, varying row/column spacing, as well as nonrigid random fluctuations of the grid nodes. The methodology is demonstrated in two case studies concerning (1) localization of DNA signals in hybridization filters and (2) localization of knit units in textile samples.

Clustering on coloured lattices

Abstract: This paper is concerned with nearest-neighbour systems on the coloured lattice (unordered state space). It extends the paper of Strauss (1975) on clustering in the two-colour case. Comparison is made with the more general methods of Besag (1974). Some tests are developed, and illustrated with an example. NEAREST-NEIGHBOUR SYSTEM; MARKOV RANDOM FIELD; CLUSTERING; QUALITATIVE DATA

Highway traffic flow prediction using support vector regression and Bayesian classifier

Abstract: With the vast availability of traffic sensing data on highway, real-time traffic flow prediction is essential part of transportation, traffic control, reports of accidents and intelligent transportation systems. To satisfy the demand of traffic flow prediction, this paper presents the method of real-time traffic flow prediction based on Bayesian classifier and support vector regression (SVR). We first model the traffic flow and its relations on the roads using 3D Markov random fields in spatiotemporal domain. Based on their relations, we define cliques as combination of current road and its neighbors. The dependencies on the defined cliques are estimated by using multiple linear regression and SVR. Finally, the traffic flow at next time stamp is predicted by finding the speed level with decreasing the energy function. To evaluate the performance of the proposed method, it was tested on traffic data obtained from Gyeongbu expressway. The experimental results showed that the approach using SVR-based estimation had superior accuracy than linear-based regression.