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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: A class of Random Field model, defined on a multiresolution array is used in the segmentation of gray level and textured images, using a simple boundary process to give accurate results even at low resolutions, and consequently at very low computational cost.
Abstract: In this paper, a class of Random Field model, defined on a multiresolution array is used in the segmentation of gray level and textured images. The novel feature of one form of the model is that it is able to segment images containing unknown numbers of regions, where there may be significant variation of properties within each region. The estimation algorithms used are stochastic, but because of the multiresolution representation, are fast computationally, requiring only a few iterations per pixel to converge to accurate results, with error rates of 1-2 percent across a range of image structures and textures. The addition of a simple boundary process gives accurate results even at low resolutions, and consequently at very low computational cost.

88 citations

Journal ArticleDOI
TL;DR: This letter proposes a very simple but effective supervised band selection algorithm based on the local spatial information of the hyperspectral image and wrapper method that consistently outperforms the classical wrapper method.
Abstract: In order to alleviate the subsequent computation burden and storage requirement, band selection has been widely adopted to reduce the dimensionality of hyperspectral images, and the current methods mainly consist of the supervised and the unsupervised. Although these supervised methods have better performance, those unsupervised methods dominate the band selection field. In this letter, based on the unique properties of hyperspectral images, we propose a very simple but effective supervised band selection algorithm based on the local spatial information of the hyperspectral image and wrapper method. By using both the information of labeled and unlabeled pixels of the hyperspectral image, our proposed algorithm consistently outperforms the classical wrapper method. We use five widely used real hyperspectral data to demonstrate the effectiveness of our proposed algorithms. We also analyze the relationship between our band selection algorithm and the well-known Markov random field classifier.

88 citations

Journal ArticleDOI
TL;DR: This work model multi-scale subbands as a product of an exponentiated homogeneous Gaussian Markov random field and a second independent hGMRF and shows that parameter estimation for this model is feasible and that samples drawn from a FoGSM model have marginal and joint statistics similar to subband coefficients of photographic images.
Abstract: The local statistical properties of photographic images, when represented in a multi-scale basis, have been described using Gaussian scale mixtures. Here, we use this local description as a substrate for constructing a global field of Gaussian scale mixtures (FoGSM). Specifically, we model multi-scale subbands as a product of an exponentiated homogeneous Gaussian Markov random field (hGMRF) and a second independent hGMRF. We show that parameter estimation for this model is feasible, and that samples drawn from a FoGSM model have marginal and joint statistics similar to subband coefficients of photographic images. We develop an algorithm for removing additive Gaussian white noise based on the FoGSM model, and demonstrate denoising performance comparable with state-of-the-art methods.

88 citations

Journal ArticleDOI
TL;DR: A multivariate hierarchical approach is developed, at the heart of which is a new representation of a multivariate Markov random field that allows for flexible modeling of the multivariate spatial dependencies, including the cross-dependencies between variables.
Abstract: Climate models have become an important tool in the study of climate and climate change, and ensemble experiments consisting of multiple climate-model runs are used in studying and quantifying the uncertainty in climate-model output. However, there are often only a limited number of model runs available for a particular experiment, and one of the statistical challenges is to characterize the distribution of the model output. To that end, we have developed a multivariate hierarchical approach, at the heart of which is a new representation of a multivariate Markov random field. This approach allows for flexible modeling of the multivariate spatial dependencies, including the cross-dependencies between variables. We demonstrate this statistical model on an ensemble arising from a regional-climate-model experiment over the western United States, and we focus on the projected change in seasonal temperature and precipitation over the next 50 years.

87 citations

Journal ArticleDOI
01 Jan 2009
TL;DR: An integrated system for emotion detection is presented, taking into account the fact that emotions are most widely represented with eye and mouth expressions, and it is consisted of three modules.
Abstract: This paper presents an integrated system for emotion detection. In this research effort, we have taken into account the fact that emotions are most widely represented with eye and mouth expressions. The proposed system uses color images and it is consisted of three modules. The first module implements skin detection, using Markov random fields models for image segmentation and skin detection. A set of several colored images with human faces have been considered as the training set. A second module is responsible for eye and mouth detection and extraction. The specific module uses the HLV color space of the specified eye and mouth region. The third module detects the emotions pictured in the eyes and mouth, using edge detection and measuring the gradient of eyes' and mouth's region figure. The paper provides results from the system application, along with proposals for further research.

87 citations


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