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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: Some pseudo-likelihoods, based on the conditional density of a block of pixels, are used together with modified EM algorithms to estimate parameters from noisy images to simulate block updating for Markov random fields.
Abstract: This paper presents a simulation study of block (one line or two lines of pixels) updating for Markov random fields. Point and line relaxation methods are compared. Some pseudo-likelihoods, based on the conditional density of a block of pixels, are used together with modified EM algorithms to estimate parameters from noisy images.

36 citations

Proceedings ArticleDOI
02 May 2012
TL;DR: This work extends a previously proposed framework for labeling whole brain scans by incorporating a global and stationary Markov random field that ensures the consistency of the neighbourhood relations between structures with an a-priori defined model.
Abstract: In recent years, multi-atlas segmentation has emerged as one of the most accurate techniques for the segmentation of brain magnetic resonance (MR) images, especially when combined with intensity-based refinement techniques such as graph-cut or expectation-maximization (EM) optimization. However, most of the work so far has focused on intensity-based refinement strategies for individual anatomical structures such as the hippocampus. In this work we extend a previously proposed framework for labeling whole brain scans by incorporating a global and stationary Markov random field that ensures the consistency of the neighbourhood relations between structures with an a-priori defined model. In particular we improve the segmentation result of a locally weighted multi-atlas fusion method for 41 different structures simultaneously by applying a subsequent EM optimization step. We evaluate the proposed approach on 30 manually annotated brain MR images and observe an improvement of label overlaps to a manual reference by up to 6%. We also achieved a considerably improved group separation when the proposed segmentation framework is applied to a volumetric analysis of 404 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort.

36 citations

Proceedings ArticleDOI
16 Sep 2008
TL;DR: This paper presents an edge-directed super-resolution algorithm for document images without using any training set, which creates an image with smooth regions in both the foreground and the background, while allowing sharp discontinuities across and smoothness along the edges.
Abstract: This paper presents an edge-directed super-resolution algorithm for document images without using any training set. This technique creates an image with smooth regions in both the foreground and the background, while allowing sharp discontinuities across and smoothness along the edges. Our method preserves sharp corners in text images by using the local edge direction, which is computed first by evaluating the gradient field and then taking its tangent. Super-resolution of document images is characterized by bimodality, smoothness along the edges as well as subsampling consistency. These characteristics are enforced in a Markov random field (MRF) framework by defining an appropriate energy function. In our method, subsampling of super-resolution image will return the original low-resolution one, proving the correctness of the method. The super-resolution image, is generated by iteratively reducing this energy function. Experimental results on a variety of input images, demonstrate the effectiveness of our method for document image super-resolution.

36 citations

Journal ArticleDOI
TL;DR: A new multiscale Markov segmentation model for multiband images is developed using quadtree multiple resolution analysis of a multiband image, which uses both inter- and intra-scale spatial Markov statistical dependencies.

36 citations

Journal ArticleDOI
TL;DR: The techniques presented here to datasets on cancer mortality and late detection in the state of Minnesota will typically lead to more accurate posterior estimates, and they are sometimes also far more efficient in terms of the number of effective samples generated per second.
Abstract: Spatial Poisson models for areal count data use nonstationary “intrinsic autoregressions,” also often referred to as “conditionally autoregressive” (CAR) models. Bayesian inference for these models has generally involved using single parameter updating Markov chain Monte Carlo algorithms, which often exhibit slow mixing (i.e., poor convergence) properties. These spatial models are richly parameterized and lend themselves to the structured Markov chain Monte Carlo (SMCMC) algorithms. SMCMC provides a simple, general, and flexible framework for accelerating convergence in an MCMC sampler by providing a systematic way to block groups of similar parameters while taking full advantage of the posterior correlation structure induced by the model and data. Among the SMCMC strategies considered here are blocking using different size blocks (grouping by geographical region), reparameterization, updating jointly with and without model hyperparameters, “oversampling” some of the model parameters, and “pilot adaptatio...

36 citations


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