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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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Journal ArticleDOI
Christian Wolf1
TL;DR: The proposed method for blind document bleed-through removal based on separate Markov Random Field regularization for the recto and for the verso side, where separate priors are derived from the full graph, shows an improvement of character recognition results compared to other restoration methods.
Abstract: We present a new method for blind document bleed-through removal based on separate Markov random field (MRF) regularization for the recto and for the verso side, where separate priors are derived from the full graph. The segmentation algorithm is based on Bayesian maximum a posteriori (MAP) estimation. The advantages of this separate approach are the adaptation of the prior to the contents creation process (e.g., superimposing two handwritten pages), and the improvement of the estimation of the recto pixels through an estimation of the verso pixels covered by recto pixels; moreover, the formulation as a binary labeling problem with two hidden labels per pixels naturally leads to an efficient optimization method based on the minimum cut/maximum flow in a graph. The proposed method is evaluated on scanned document images from the 18th century, showing an improvement of character recognition results compared to other restoration methods.

64 citations

Journal ArticleDOI
TL;DR: Experiments conducted on a set of five real remote sensing images acquired by different sensors and referring to different kinds of changes show the high robustness of the proposed unsupervised change detection approach.
Abstract: The most common methodology to carry out an automatic unsupervised change detection in remotely sensed imagery is to find the best global threshold in the histogram of the so-called difference image. The unsupervised nature of the change detection process, however, makes it nontrivial to find the most appropriate thresholding algorithm for a given difference image, because the best global threshold depends on its statistical peculiarities, which are often unknown. In this letter, a solution to this issue based on the fusion of an ensemble of different thresholding algorithms through a Markov random field framework is proposed. Experiments conducted on a set of five real remote sensing images acquired by different sensors and referring to different kinds of changes show the high robustness of the proposed unsupervised change detection approach

64 citations

Proceedings ArticleDOI
01 Dec 1976
TL;DR: In this article, the role of the Markov random field in recursive image modeling is presented, since spectral factorization in two-or higher-dimensions generally results in infinite order factors, it is necessary to perform Markov modeling after spectral factorisation.
Abstract: The theory of two-dimensional spectral factorization is reviewed in the context of recursive modeling. The role of the Markov random field in recursive image modeling is then presented, Since spectral factorization in two-or higher-dimensions generally results in infinite order factors, it is necessary to perform Markov modeling after spectral factorization. The above concepts are then applied to the problem of Kalman filtering of images.

64 citations

Journal ArticleDOI
TL;DR: A probabilistic framework for modeling single-trial functional magnetic resonance (fMR) images based on a parametric model for the hemodynamic response and Markov random field image models.
Abstract: Describes a probabilistic framework for modeling single-trial functional magnetic resonance (fMR) images based on a parametric model for the hemodynamic response and Markov random field (MRF) image models. The model is fitted to image data by maximizing a lower bound on the log likelihood. The result is an approximate maximum a posteriori estimate of the joint distribution over the model parameters and pixel labels. Examples show how this technique can used to segment two-dimensional (2-D) fMR images, or parts thereof, into regions with different characteristics of their hemodynamic response.

64 citations

Patent
10 Oct 2006
TL;DR: In this paper, a Markov Random Field (MRF) approach is used to detect change in video streams in an indoor environment, where information from different sources are combined with additional constraints to provide the final detection map.
Abstract: A system and method for automated and/or semi-automated analysis of video for discerning patterns of interest in video streams. In a preferred embodiment, the present invention is directed to identifying patterns of interest in indoor settings. In one aspect, the present invention deals with the change detection problem using a Markov Random Field approach where information from different sources are naturally combined with additional constraints to provide the final detection map. A slight modification is made of the regularity term within the MRF model that accounts for real-discontinuities in the observed data. The defined objective function is implemented in a multi-scale framework that decreases the computational cost and the risk of convergence to local minima. To achieve real-time performance, fast deterministic relaxation algorithms are used to perform the minimization. The crowdedness measure used is a geometric measure of occupancy that is quasi-invariant to objects translating on the platform.

64 citations


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