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Compound GaussMarkov random fields for image estimation and restoration
 Rensselaer Polytechnic Institute
, 1988
"... AbstmctThis paper is concerned with algorithms for obtaining approximations to statistically optimal estimates for images modeled as compound GaussMarkov random fields. We consider both the maximum aposteriori probability (MAP) estimate and the minimum meansquared error (MMSE) estimate for both ..."
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Cited by 49 (0 self)
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AbstmctThis paper is concerned with algorithms for obtaining approximations to statistically optimal estimates for images modeled as compound GaussMarkov random fields. We consider both the maximum aposteriori probability (MAP) estimate and the minimum meansquared error (MMSE) estimate
Multichannel Image Restoration Using Compound GaussMarkov Random Fields
 IEEE Trans. Image Proc
, 2000
"... In this paper, a solution to the multichannel image restoration problem is provided using compound Gauss Markov random fields. For the single channel deblurring problem the convergence of the Simulated Annealing (SA) and Iterative Conditional Mode (ICM) algorithms has not been established. We propos ..."
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Cited by 24 (2 self)
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In this paper, a solution to the multichannel image restoration problem is provided using compound Gauss Markov random fields. For the single channel deblurring problem the convergence of the Simulated Annealing (SA) and Iterative Conditional Mode (ICM) algorithms has not been established. We
Color Image Restoration Using Compound GaussMarkov Random Fields
 in Proc. X Eur. Signal Processing Conf. (EUSIPCO’2000
, 2000
"... In this work we extend the use of Compound Gauss Markov Random Fields to the restoration of color images. While most of the work in color image restoration is concentrated on enforcing similarity between the intensity values of the pixels in the image bands, we propose combining information by means ..."
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Cited by 1 (1 self)
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In this work we extend the use of Compound Gauss Markov Random Fields to the restoration of color images. While most of the work in color image restoration is concentrated on enforcing similarity between the intensity values of the pixels in the image bands, we propose combining information
Transmission Tomography Reconstruction Using Compound GaussMarkov Random Fields and
"... Emission tomography images are degraded due to the presence of noise and several physical factors, like attenuation and scattering. ..."
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Emission tomography images are degraded due to the presence of noise and several physical factors, like attenuation and scattering.
Inducing Features of Random Fields
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1997
"... We present a technique for constructing random fields from a set of training samples. The learning paradigm builds increasingly complex fields by allowing potential functions, or features, that are supported by increasingly large subgraphs. Each feature has a weight that is trained by minimizing the ..."
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Cited by 664 (14 self)
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the KullbackLeibler divergence between the model and the empirical distribution of the training data. A greedy algorithm determines how features are incrementally added to the field and an iterative scaling algorithm is used to estimate the optimal values of the weights. The random field models and techniques
Markov Random Field Models in Computer Vision
, 1994
"... . A variety of computer vision problems can be optimally posed as Bayesian labeling in which the solution of a problem is defined as the maximum a posteriori (MAP) probability estimate of the true labeling. The posterior probability is usually derived from a prior model and a likelihood model. The l ..."
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Cited by 515 (18 self)
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. The latter relates to how data is observed and is problem domain dependent. The former depends on how various prior constraints are expressed. Markov Random Field Models (MRF) theory is a tool to encode contextual constraints into the prior probability. This paper presents a unified approach for MRF modeling
Segmentation of brain MR images through a hidden Markov random field model and the expectationmaximization algorithm
 IEEE TRANSACTIONS ON MEDICAL. IMAGING
, 2001
"... The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, being a histogrambased model, the FM has an intrinsic limi ..."
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Cited by 619 (14 self)
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based methods produce unreliable results. In this paper, we propose a novel hidden Markov random field (HMRF) model, which is a stochastic process generated by a MRF whose state sequence cannot be observed directly but which can be indirectly estimated through observations. Mathematically, it can be shown
Unsupervised Image Restoration and Edge Location Using Compound GaussMarkov Random Fields and the MDL Principle
 IEEE Trans. Image Processing
, 1997
"... Discontinuitypreserving Bayesian image restoration typically involves two Markov random fields: one representing the image intensities/gray levels to be recovered and another one signaling discontinuities/edges to be preserved. The usual strategy is to perform joint maximum a posteriori (MAP) estim ..."
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Cited by 27 (10 self)
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) estimation of the image and its edges, which requires the specification of priors for both fields. In this paper, instead of taking an edge prior, we interpret discontinuities (in fact their locations) as deterministic unknown parameters of the compound GaussMarkov random field (CGMRF), which is assumed
Light Field Rendering
, 1996
"... A number of techniques have been proposed for flying through scenes by redisplaying previously rendered or digitized views. Techniques have also been proposed for interpolating between views by warping input images, using depth information or correspondences between multiple images. In this paper, w ..."
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Cited by 1354 (22 self)
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A number of techniques have been proposed for flying through scenes by redisplaying previously rendered or digitized views. Techniques have also been proposed for interpolating between views by warping input images, using depth information or correspondences between multiple images. In this paper
Walksummable GaussMarkov random fields
, 2005
"... This note introduces an interesting class of GaussMarkov Random Fields designated as walksummable. Several equivalent characterizations of this class of GMRFs are established. Also, several important subclasses of GMRFs are identified as being walksummable. These include (i) diagonally dominant, ..."
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Cited by 3 (1 self)
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This note introduces an interesting class of GaussMarkov Random Fields designated as walksummable. Several equivalent characterizations of this class of GMRFs are established. Also, several important subclasses of GMRFs are identified as being walksummable. These include (i) diagonally dominant
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