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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.


Papers
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Proceedings Article
08 Dec 2014
TL;DR: In this article, a hybrid architecture that consists of a deep Convolu-tional Network and a Markov Random Field (MRF) was proposed for articulated human pose estimation in monocular images.
Abstract: This paper proposes a new hybrid architecture that consists of a deep Convolu-tional Network and a Markov Random Field We show how this architecture is successfully applied to the challenging problem of articulated human pose estimation in monocular images The architecture can exploit structural domain constraints such as geometric relationships between body joint locations We show that joint training of these two model paradigms improves performance and allows us to significantly outperform existing state-of-the-art techniques

601 citations

Journal ArticleDOI
TL;DR: An algorithm, referred to as spatio-temporal Markov random field, for traffic images at intersections, that models a tracking problem by determining the state of each pixel in an image and its transit, and how such states transit along both the x-y image axes as well as the time axes.
Abstract: We have developed an algorithm, referred to as spatio-temporal Markov random field, for traffic images at intersections. This algorithm models a tracking problem by determining the state of each pixel in an image and its transit, and how such states transit along both the x-y image axes as well as the time axes. Our algorithm is sufficiently robust to segment and track occluded vehicles at a high success rate of 93%-96%. This success has led to the development of an extendable robust event recognition system based on the hidden Markov model (HMM). The system learns various event behavior patterns of each vehicle in the HMM chains and then, using the output from the tracking system, identifies current event chains. The current system can recognize bumping, passing, and jamming. However, by including other event patterns in the training set, the system can be extended to recognize those other events, e.g., illegal U-turns or reckless driving. We have implemented this system, evaluated it using the tracking results, and demonstrated its effectiveness.

545 citations

Journal ArticleDOI
TL;DR: A fully automated algorithm for segmentation of multiple sclerosis lesions from multispectral magnetic resonance (MR) images that performs intensity-based tissue classification using a stochastic model and simultaneously detects MS lesions as outliers that are not well explained by the model.
Abstract: This paper presents a fully automated algorithm for segmentation of multiple sclerosis (MS) lesions from multispectral magnetic resonance (MR) images. The method performs intensity-based tissue classification using a stochastic model for normal brain images and simultaneously detects MS lesions as outliers that are not well explained by the model. It corrects for MR field inhomogeneities, estimates tissue-specific intensity models from the data itself, and incorporates contextual information in the classification using a Markov random field. The results of the automated method are compared with lesion delineations by human experts, showing a high total lesion load correlation. When the degree of spatial correspondence between segmentations is taken into account, considerable disagreement is found, both between expect segmentations, and between expert and automatic measurements.

539 citations

Journal ArticleDOI
TL;DR: The utility of a new Multimodal Surface Matching (MSM) algorithm capable of driving alignment using a wide variety of descriptors of brain architecture, function and connectivity is demonstrated.

539 citations

Proceedings ArticleDOI
13 Oct 2003
TL;DR: This work compares the belief propagation algorithm and the graph cuts algorithm on the same MRF's, which have been created for calculating stereo disparities, and finds that the labellings produced by the two algorithms are comparable.
Abstract: Recent stereo algorithms have achieved impressive results by modelling the disparity image as a Markov Random Field (MRF). An important component of an MRF-based approach is the inference algorithm used to find the most likely setting of each node in the MRF. Algorithms have been proposed which use graph cuts or belief propagation for inference. These stereo algorithms differ in both the inference algorithm used and the formulation of the MRF. It is unknown whether to attribute the responsibility for differences in performance to the MRF or the inference algorithm. We address this through controlled experiments by comparing the belief propagation algorithm and the graph cuts algorithm on the same MRF's, which have been created for calculating stereo disparities. We find that the labellings produced by the two algorithms are comparable. The solutions produced by graph cuts have a lower energy than those produced with belief propagation, but this does not necessarily lead to increased performance relative to the ground truth.

538 citations


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