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.

DEUM : a framework for an estimation of distribution algorithm based on Markov random fields

Abstract: Estimation of Distribution Algorithms (EDAs) belong to the class of population based optimisation algorithms They are motivated by the idea of discovering and exploiting the interaction between variables in the solution They estimate a probability distribution from population of solutions, and sample it to generate the next population Many EDAs use probabilistic graphical modelling techniques for this purpose In particular, directed graphical models (Bayesian networks) have been widely used in EDA This thesis proposes an undirected graphical model (Markov Random Field (MRF)) approach to estimate and sample the distribution in EDAs The interaction between variables in the solution is modelled as an undirected graph and the joint probability of a solution is factorised as a Gibbs distribution The thesis describes a model of fitness function that approximates the energy in the Gibbs distribution, and shows how this model can be fitted to a population of solutions to estimate the parameters of the MRF The estimated MRF is then sampled to generate the next population This approach is applied to estimation of distribution in a general framework of an EDA, called Distribution Estimation using Markov Random Fields (DEUM) The thesis then proposes several variants of DEUM using different sampling techniques and tests their performance on a range of optimisation problems The results show that, for most of the tested problems, the DEUM algorithms significantly outperform other EDAs, both in terms of number of fitness evaluations and the quality of the solutions found by them There are two main explanations for the success of DEUM algorithms Firstly, DEUM builds a model of fitness function to approximate the MRF This contrasts with other EDAs, which build a model of selected solutions This allows DEUM to use fitness in variation part of the evolution Secondly, DEUM exploits the temperature coefficient in the Gibbs distribution to regulate the behaviour of the algorithm In particular, with higher temperature, the distribution is closer to being uniform and with lower temperature it concentrates near some global optima This gives DEUM an explicit control over the convergence of the algorithm, resulting in better optimisation

Semantic segmentation of 3D textured meshes for urban scene analysis

Abstract: Classifying 3D measurement data has become a core problem in photogram-metry and 3D computer vision, since the rise of modern multiview geometry techniques, combined with affordable range sensors. We introduce a Markov Random Field-based approach for segmenting textured meshes generated via multi-view stereo into urban classes of interest. The input mesh is first partitioned into small clusters, referred to as superfacets, from which geometric and photometric features are computed. A random forest is then trained to predict the class of each superfacet as well as its similarity with the neighboring superfacets. Similarity is used to assign the weights of the Markov Random Field pairwise-potential and accounts for contextual information between the classes. The experimental results illustrate the efficacy and accuracy of the proposed framework.

Joint tumor segmentation and dense deformable registration of brain MR images

Abstract: In this paper we propose a novel graph-based concurrent registration and segmentation framework. Registration is modeled with a pairwise graphical model formulation that is modular with respect to the data and regularization term. Segmentation is addressed by adopting a similar graphical model, using image-based classification techniques while producing a smooth solution. The two problems are coupled via a relaxation of the registration criterion in the presence of tumors as well as a segmentation through a registration term aiming the separation between healthy and diseased tissues. Efficient linear programming is used to solve both problems simultaneously. State of the art results demonstrate the potential of our method on a large and challenging low-grade glioma data set.

Belief Propagation on the GPU for Stereo Vision

Abstract: The power of Markov random field formulations of lowlevel vision problems, such as stereo, has been known for some time. However, recent advances, both algorithmic and in processing power, have made their application practical. This paper presents a novel implementation of Bayesian belief propagation for graphics processing units found in most modern desktop and notebook computers, and applies it to the stereo problem. The stereo problem is used for comparison to other BP algorithms.