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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.


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Journal ArticleDOI
TL;DR: A fully automated Markov random field model that is used to assign labels to all segmented regions in the full scene has been designed and implemented and is the first known successful end-to-end process for operational SAR sea-ice image classification.
Abstract: Thousands of spaceborne synthetic aperture radar (SAR) sea-ice images are systematically processed every year in support of operational activities such as ship navigation and environmental monitoring. An automated approach that generates pixel-level sea-ice image classification is required since manual pixel-level classification is not feasible. Currently, using a standardized approach, trained ice analysts manually segment full SAR scenes into smaller polygons to record ice types and concentrations. Using these data, pixel-level classification can be achieved by initial unsupervised segmentation of each polygon, followed by automatic sea-ice labeling of the full scene. A fully automated Markov random field model that is used to assign labels to all segmented regions in the full scene has been designed and implemented. This approach is the first known successful end-to-end process for operational SAR sea-ice image classification. In addition, a novel performance evaluation framework has been developed to validate the segmentation and labeling of SAR sea-ice images. A trained sea-ice expert has conducted an arms length evaluation using this framework to generate a set of full-scene reference images used for testing. Testing demonstrates operational success of the labeling approach.

104 citations

Proceedings ArticleDOI
27 Jun 2007
TL;DR: Presented at the 2007 Robotics: Science and Systems Conference III (RSS), 27-30 June 2007, Atlanta, GA.
Abstract: Presented at the 2007 Robotics: Science and Systems Conference III (RSS), 27-30 June 2007, Atlanta, GA.

104 citations

Proceedings ArticleDOI
20 Jun 2011
TL;DR: A novel global stereo model that makes use of constraints from points with known depths, i.e., the Ground Control Points (GCPs) as referred to in stereo literature, is presented.
Abstract: We present a novel global stereo model that makes use of constraints from points with known depths, i.e., the Ground Control Points (GCPs) as referred to in stereo literature. Our formulation explicitly models the influences of GCPs in a Markov Random Field. A novel GCPs-based regu-larization term is naturally integrated into our global optimization framework in a principled way using the Bayes rule. The optimal solution of the inference problem can be approximated via existing energy minimization techniques such as graph cuts used in this paper. Our generic probabilistic framework allows GCPs to be obtained from various modalities and provides a natural way to integrate the information from multiple sensors. Quantitative evaluations demonstrate the effectiveness of the proposed formulation for regularizing the ill-posed stereo matching problem and improving reconstruction accuracy.

104 citations

Journal ArticleDOI
TL;DR: A Bayesian approach for generalized linear models proposed by the author which uses a Markov random field to model the coefficients' spatial dependency and proves a result showing the equivalence between this model and other usual spatial regression models.
Abstract: Many spatial regression problems using area data require more flexible forms than the usual linear predictor for modelling the dependence of responses on covariates. One direction for doing this is to allow the coefficients to vary as smooth functions of the area's geographical location. After presenting examples from the scientific literature where these spatially varying coefficients are justified, we briefly review some of the available alternatives for this kind of modelling. We concentrate on a Bayesian approach for generalized linear models proposed by the author which uses a Markov random field to model the coefficients' spatial dependency. We show that, for normally distributed data, Gibbs sampling can be used to sample from the posterior and we prove a result showing the equivalence between our model and other usual spatial regression models. We illustrate our approach with a number of rather complex applied problems, showing that the method is computationally feasible and provides useful insights in substantive problems. Copyright © 2003 John Wiley & Sons, Ltd.

104 citations

Journal ArticleDOI
TL;DR: A nonlinear color transform relevant for hue segmentation is derived from a logarithmic model, and a hierarchical segmentation scheme is based on Markov random field modeling, that combines hue and motion detection within a spatiotemporal neighborhood.
Abstract: This paper deals with the low-level joint processing of color and motion for robust face analysis within a feature-based approach. To gain robustness and contrast under unsupervised viewing conditions, a nonlinear color transform relevant for hue segmentation is derived from a logarithmic model. A hierarchical segmentation scheme is based on Markov random field modeling, that combines hue and motion detection within a spatiotemporal neighborhood. Relevant face regions are segmented without parameter tuning. The accuracy of the label fields enables not only face detection and tracking but also geometrical measurements on facial feature edges, such as lips or eyes. Results are shown both on typical test sequences and on various sequences acquired from micro- or mobile-cameras. The efficiency of the method makes it suitable for real-time applications aiming at audiovisual communication in unsupervised environments.

103 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20241
202330
2022128
202196
2020173
2019204