Vector field visualization generates an image to convey the information existing in the data. We use Markov Random Field texture synthesis methods to generate the visualization from a set of example textures. The examples textures are chosen according to the vector data for each pixel of the output. This leads to dense visualizations with arbitrary example textures.

Traditionally, the goal of image segmentation has been to produce a single partition of an image. This partition is compared to some ‘ground truth’, or human approved partition, to evaluate the performance of the algorithm. This paper utilizes a framework for considering a range of possible partitions of the image to compute a probability distribution on the space of possible partitions of the ...

In this paper we propose a Markov random field with asymmetric Markov parameters to model the spatial and topological relationships between objects in structured scenes. The field is formulated in terms of conditional probabilities learnt from a set of training images. A locally consistent labelling of new scenes is achieved by relaxing the Markov random field directly using these conditional p...

In this work we present Cutting Plane Inference (CPI) for MAP inference in Markov Logic. CPI incrementally solves partial Ground Markov Networks, adding formulae only if they are violated in the current solution. We show dramatic improvements in terms of e ciency, and discuss scenarios where CPI is likely to be fast.

A new approach to multi−resolution modeling of images is introduced and applied to the task of semi−unsupervised texture segmentation using Gaussian Markov random fields (GMRFs). It is shown that traditional GMRF modeling of multi−resolution coefficients is incapable of accounting for the non−Gaussian statistics which often characterize the multi−resolution coefficients. On the other hand, the ...

We consider the problem of learning the structure of Ising models (pairwise binary Markov random fields) from i.i.d. samples. While several methods have been proposed to accomplish this task, their relative merits and limitations remain somewhat obscure. By analyzing a number of concrete examples, we show that low-complexity algorithms often fail when the Markov random field develops long-range...

This article presents a novel method for estimating the dense three-dimensional motion of a scene from multiple cameras. Our method employs an interconnected patch model of the scene surfaces. The interconnected nature of the model means that we can incorporate prior knowledge about neighbouring scene motions through the use of a Markov Random Field, whilst the patchbased nature of the model al...