Topic
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.
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TL;DR: In this paper, a Bayesian algorithm for linear spectral unmixing of hyperspectral images that accounts for anomalies present in the data is presented, where the pixel reflectances are linear mixtures of unknown endmembers, corrupted by an additional nonlinear term modeling anomalies and additive Gaussian noise.
Abstract: This paper presents a Bayesian algorithm for linear spectral unmixing of hyperspectral images that accounts for anomalies present in the data. The model proposed assumes that the pixel reflectances are linear mixtures of unknown endmembers, corrupted by an additional nonlinear term modeling anomalies, and additive Gaussian noise. A Markov random field is used for anomaly detection based on the spatial and spectral structures of the anomalies. This allows outliers to be identified in particular regions and wavelengths of the data cube. A Bayesian algorithm is proposed to estimate the parameters involved in the model yielding a joint linear unmixing and anomaly detection algorithm. Simulations conducted with synthetic and real hyperspectral images demonstrate the accuracy of the proposed unmixing and outlier detection strategy for the analysis of hyperspectral images.
50 citations
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07 Jun 1999
TL;DR: An algorithm for speaker's lip contour extraction using spatially varying coefficients and a Bayesian approach segments the mouth area using Markov random field modelling results in an accurate lip shape with inner and outer borders.
Abstract: An algorithm for speaker's lip contour extraction is presented in this paper. A color video sequence of the speaker's face is acquired under natural lighting conditions and without any particular make-up. First, a logarithmic color transform is performed from RGB to HI (hue, intensity) color space. A Bayesian approach segments the mouth area using Markov random field modelling. Motion is combined with red hue lip information into a spatiotemporal neighbourhood. Simultaneously, a region of interest and relevant boundary points are automatically extracted. Next, an active contour using spatially varying coefficients is initialised with the results of the preprocessing stage. Finally, an accurate lip shape with inner and outer borders is obtained with good quality results in this challenging situation.
50 citations
01 Jan 2005
TL;DR: A new unsupervised MR image segmentation method based on self-organizing feature map (SOFM) network is presented, which includes spatial constraints by using a Markov Random Field (MRF) model.
Abstract: Magnetic resonance imaging (MRI) is an advanced medical imaging technique providing rich information about the human soft tissue anatomy. The goal of magnetic resonance (MR) image segmentation is to accurately identify the principal tissue structures in these image volumes. A new unsupervised MR image segmentation method based on self-organizing feature map (SOFM) network is presented. The algorithm includes spatial constraints by using a Markov Random Field (MRF) model. The MRF term introduces the prior distribution with clique potentials and thus improves the segmentation results without having extra data samples in the training set or a complicated network structure. The simulation results demonstrate that the proposed algorithm is promising.
50 citations
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TL;DR: The proposed method derives suitable initial cluster parameters from a set of homogeneous regions, and estimates the number of clusters using the pseudolikelihood information criterion (PLIC) using a well-known polarimetric SAR image of Flevoland in the Netherlands.
Abstract: Markov random field (MRF) clustering, utilizing both spectral and spatial interpixel dependency information, often improves classification accuracy for remote sensing images, such as multichannel polarimetric synthetic aperture radar (SAR) images. However, it is heavily sensitive to initial conditions such as the choice of the number of clusters and their parameters. In this paper, an initialization scheme for MRF clustering approaches is suggested for remote sensing images. The proposed method derives suitable initial cluster parameters from a set of homogeneous regions, and estimates the number of clusters using the pseudolikelihood information criterion (PLIC). The method works best for an image consisting of many large homogeneous regions, such as agricultural crops areas. It is illustrated using a well-known polarimetric SAR image of Flevoland in the Netherlands. The experiment shows a superior performance compared to several other methods, such as fuzzy C-means and iterated conditional modes (ICM) clustering.
50 citations
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TL;DR: The authors propose a progressive minimization procedure of this energy function starting from initial reliably labeled pixels and involving only local computation.
Abstract: The early and accurate segmentation of low clouds during the night-time is an important task for nowcasting. It requires that observations can be acquired at a sufficient time rate as provided by the geostationary METEOSAT satellite over Europe. However, the information supplied by the single infrared METEOSAT channel available by night is not sufficient to discriminate between low clouds and ground during night from a single image. To tackle this issue, the authors consider several sources of information extracted from an infrared image sequence. Indeed, they exploit both relevant local motion-based measurements, intensity images and thermal parameters estimated over blocks, along with local contextual information. A statistical contextual labeling process in two classes, involving "low clouds" and "clear sky," is performed on the warmer pixels. It is formulated within a Bayesian estimation framework associated with Markov random field (MRF) models. This comes to minimize a global energy function comprising three terms: two data-driven terms (thermal and motion-based ones) and a regularization term expressing a priori knowledge on the label field (expected spatial contextual properties). The authors propose a progressive minimization procedure of this energy function starting from initial reliably labeled pixels and involving only local computation.
50 citations