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.

A Markov random field model for object matching under contextual constraints

Abstract: This paper presents a Markov random field (MRF) model for object recognition in high level vision. The labeling state of a scene in terms of a model object is considered as an MRF or couples MRFs. Within the Bayesian framework the optimal solution is defined as the maximum a posteriori (MAP) estimate of the MRF. The posterior distribution is derived based on sound mathematical principles from theories of MRF and probability, which is in contrast to heuristic formulations. An experimental result is presented. >

Detecting principal directions of unknown environments

Abstract: Apparatus and methods according to some embodiments of the present invention use a graphical model, such as a Markov random field model, to represent principal driving directions within an environment. The model has a plurality of nodes representing spatial locations within the environment, and the principal direction for each node is determined probabilistically using linear features detected within an image of the environment. Apparatus and methods according to embodiments of the present invention can be used in improved autonomous navigation systems, such as robotic vehicles.

A Bayesian segmentation methodology for parametric image models

Abstract: Region-based image segmentation methods require some criterion for determining when to merge regions. This paper presents a novel approach by introducing a Bayesian probability of homogeneity in a general statistical context. The authors' approach does not require parameter estimation and is therefore particularly beneficial for cases in which estimation-based methods are most prone to error: when little information is contained in some of the regions and, therefore, parameter estimates are unreliable. The authors apply this formulation to three distinct parametric model families that have been used in past segmentation schemes: implicit polynomial surfaces, parametric polynomial surfaces, and Gaussian Markov random fields. The authors present results on a variety of real range and intensity images. >

A spectral mixture process conditioned by Gibbs-based partitioning

Abstract: An enhanced method of spectral mixture analysis is investigated for hyperspectral imagery of moderate-to-high scene complexity, where either a large set of fundamental materials may exist throughout, or where some of the fundamental members have spectra that are similar to each other. For a complex scene, the use of one large set of fundamental materials as the set of "endmembers" for performing spectral unmixing can cause unreliable estimates of material compositions at sites within the scene. In such cases, partitioning this large set of endmembers into a number of smaller sets is appropriate, where the smaller sets are associated with certain regions in a scene. Herein, a Gibbs-based algorithm is developed to partition hyperspectral imagery into regions of similarity. This partitioning algorithm provides an estimator of an underlying and unobserved process called a "partition process" that coexists with other underlying (and unobserved) processes, one of which is called a "spectral mixing process." The algorithm exploits the properties of a Markov random field (MRF) and the associated Gibbs equivalence theorem, using a suitably defined graph structure and a Gibbs distribution to model the partition process. Consequently, spatial consistency is imposed on the spectral content of sites in each partition. The enhanced spectral mixing process is then computed as a linear mixture model that is conditioned on the partition process. Experiments are performed using scenes of HYDICE imagery to validate the algorithm, where spectral mixture analysis is performed with and without conditioning on the partitioning process.