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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
TL;DR: The experimental study established that the proposed two stage approach extracted efficiently the contrast enhanced regions from the MRA and T1C brain images.

57 citations

Journal ArticleDOI
TL;DR: A method for behavior modeling and abnormal events detection which uses low-level features, and when the spatio-temporal volume is sufficiently large, the co-occurrence distribution contains the average normal path followed by moving objects.

57 citations

Journal ArticleDOI
TL;DR: In this article, a change point estimator is proposed for high dimensional Markov random field models, which is obtained by maximizing a profile penalized pseudolikelihood function under a sparsity assumption.
Abstract: Summary The paper investigates a change point estimation problem in the context of high dimensional Markov random-field models. Change points represent a key feature in many dynamically evolving network structures. The change point estimate is obtained by maximizing a profile penalized pseudolikelihood function under a sparsity assumption. We also derive a tight bound for the estimate, up to a logarithmic factor, even in settings where the number of possible edges in the network far exceeds the sample size. The performance of the estimator proposed is evaluated on synthetic data sets and is also used to explore voting patterns in the US Senate in the 1979–2012 period.

57 citations

Journal ArticleDOI
TL;DR: A novel single image Bayesian super-resolution algorithm where the hyperspectral image (HSI) is the only source of information is proposed and it is shown that the proposed method outperforms the state of the art methods in terms of quality while preserving the spectral consistency.
Abstract: In this paper, we propose a novel single image Bayesian super-resolution (SR) algorithm where the hyperspectral image (HSI) is the only source of information. The main contribution of the proposed approach is to convert the ill-posed SR reconstruction problem in the spectral domain to a quadratic optimization problem in the abundance map domain. In order to do so, Markov random field based energy minimization approach is proposed and proved that the solution is quadratic. The proposed approach consists of five main steps. First, the number of endmembers in the scene is determined using virtual dimensionality. Second, the endmembers and their low resolution abundance maps are computed using simplex identification via the splitted augmented Lagrangian and fully constrained least squares algorithms. Third, high resolution (HR) abundance maps are obtained using our proposed maximum a posteriori based energy function. This energy function is minimized subject to smoothness, unity, and boundary constraints. Fourth, the HR abundance maps are further enhanced with texture preserving methods. Finally, HR HSI is reconstructed using the extracted endmembers and the enhanced abundance maps. The proposed method is tested on three real HSI data sets; namely the Cave, Harvard, and Hyperspectral Remote Sensing Scenes and compared with state-of-the-art alternative methods using peak signal to noise ratio, structural similarity, spectral angle mapper, and relative dimensionless global error in synthesis metrics. It is shown that the proposed method outperforms the state of the art methods in terms of quality while preserving the spectral consistency.

57 citations

Journal ArticleDOI
TL;DR: A new image model is proposed, the pseudo-Markov model, which retains the characteristics of the multispectral Markov model yet admits to a simplified parameter estimation method.
Abstract: Multispectral extensions to the traditional gray level simultaneous autoregressive (SAR) and Markov random field (MRF) models are considered. Furthermore, a new image model is proposed, the pseudo-Markov model, which retains the characteristics of the multispectral Markov model, yet admits to a simplified parameter estimation method. These models are well-suited to analysis and modeling of color images. For each model considered, procedures are developed for parameter estimation and image synthesis. Experimental results, based on known image models and natural texture samples, substantiate the validity of thee results.

57 citations


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