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An Energy-Based Model for the Image Edge-Histogram Specification Problem

IEEE TRANSACTIONS ON IMAGE PROCESSING 2012年第1期21卷 379-+页

作者： Mignotte, Max Univ Montreal Dept Informat & Rech Operat Fac Arts & Sci Montreal PQ H3C 3J7 Canada

In this correspondence, we present an original energy-based model that achieves the edge-histogram specification of a real input image and thus extends the exact specification method of the image luminance (or gray level) distribution recently proposed by Coltuc et al. Our edge-histogram specification approach is stated as an optimization problem in which each edge of a real input image will tend iteratively toward some specified gradient magnitude values given by a target edge distribution (or a normalized edge histogram possibly estimated from a target image). To this end, a hybrid optimization scheme combining a global and deterministic conjugate-gradient-based procedure and a local stochastic search using the metropolis criterion is proposed herein to find a reliable solution to our energy-based model. Experimental results are presented, and several applications follow from this procedure.

关键词： Conjugate gradient edge-histogram specification energy-based model gradient magnitude local stochastic search metropolis algorithm

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On the stability and ergodicity of adaptive scaling metropolis algorithms

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STOCHASTIC PROCESSES AND THEIR APPLICATIONS 2011年第12期121卷 2839-2860页

作者： Vihola, Matti Univ Jyvaskyla Dept Math & Stat FI-40014 Jyvaskyla Finland

The stability and ergodicity properties of two adaptive random walk metropolis algorithms are considered. Both algorithms adjust the scaling of the proposal distribution continuously based on the observed acceptance probability. Unlike the previously proposed forms of the algorithms, the adapted scaling parameter is not constrained within a predefined compact interval. The first algorithm is based on scale adaptation only, while the second one also incorporates covariance adaptation. A strong law of large numbers is shown to hold assuming that the target density is smooth enough and has either compact support or super-exponentially decaying tails. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Adaptive Markov chain Monte Carlo Law of large numbers metropolis algorithm Stability Stochastic approximation

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A Bicriteria-Optimization-Approach-Based Dimensionality-Reduction Model for the Color Display of Hyperspectral Images

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2012年第2期50卷 501-513页

作者： Mignotte, Max Univ Montreal Dept Informat & Rech Operat Fac Arts & Sci Montreal PQ H3C 3J7 Canada

This paper proposes a new nonlinear dimensionalityreduction model based on a bicriteria global optimization approach for the color display of hyperspectral images. The proposed fusion model is derived from two well-known and contradictory criteria of good visualization, which are useful in any multidimensional imagery color display, namely, accuracy, with the preservation of spectral distance criterion, and contrast, guaranteeing that colors are well distinguished or concretely allowing the good separability of each observed existing material in the final visualized color image. An internal parameter allows our algorithm to express the contribution or the importance of these two criteria for a specific application. In this framework, which also can be viewed as a classical Bayesian optimization strategy involving a tradeoff between fidelity to the unreduced (raw) spectral data and the expected highly contrasted resulting mapping, we will show that a hybrid optimization strategy, combining a global and deterministic optimization procedure and a local stochastic search using the metropolis criterion, can be exploited to efficiently minimize the complex nonlinear objective cost function related to our model. The experiments reported in this paper demonstrate that the proposed model, taking into account these two criteria of good visualization, makes easier and more reliable the interpretation and quick overview of such multidimensional hyperspectral images.

关键词： Bayesian model color display model complete graph dimensionality-reduction model FastMap optimization local exploration search low dimensional embedding metropolis algorithm multicriteria optimization multidimensional hyperspectral imagery nonstationary Markov random field model stress function

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A general Bayesian treatment for MEG source reconstruction incorporating lead field uncertainty

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NEUROIMAGE 2012年第2期60卷 1194-1204页

作者： Lopez, J. D. Penny, W. D. Espinosa, J. J. Barnes, G. R. Univ Nacl Colombia Mech Sch Fac Minas Bl M8 108 Medellin Colombia UCL Wellcome Trust Ctr Neuroimaging London WC1N 3BG England

There is uncertainty introduced when a cortical surface based model derived from an anatomical MRI is used to reconstruct neural activity with MEG data. This is a specific case of a problem with uncertainty in parameters on which M/EEG lead fields depend non-linearly. Here we present a general mathematical treatment of any such problem with a particular focus on co-registration. We use a metropolis search followed by Bayesian Model Averaging over multiple sparse prior source inversions with different headlocation/orientation parameters. Based on MEG data alone we can locate the cortex to within 4 mm at empirically realistic signal to noise ratios. We also show that this process gives improved posterior distributions on the estimated current distributions, and can be extended to make inference on the locations of local maxima by providing confidence intervals for each source. (c) 2012 Elsevier Inc. All rights reserved.

关键词： MEG inverse problem Co-registration metropolis algorithm Bayesian model averaging

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Precipitation or evapotranspiration? Bayesian analysis of potential error sources in the simulation of sub-basin discharges in the Czech Elbe River basin

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REGIONAL ENVIRONMENTAL CHANGE 2012年第3期12卷 649-661页

作者： Conradt, Tobias Koch, Hagen Hattermann, Fred F. Wechsung, Frank Potsdam Inst Climate Impact Res D-14473 Potsdam Germany Brandenburg Tech Univ Cottbus D-03046 Cottbus Germany

A global change assessment required detailed simulation of water availability in the Elbe River basin in Central Europe (148,268 kmA(2)). Using the spatially semi-distributed, eco-hydrological model SWIM, spatial calibration was applied. For 225 sub-areas covering the model domain (134,890 kmA(2)), evapotranspiration and groundwater dynamics were individually adjusted. The calibration aimed at good correspondences with long-term run-off contributions and the hydrographs for two extreme years. Measured run-off was revised from water management effects to produce quasi-natural discharges for calibration. At some gauges, there were large volume differences between these reference data and the simulations of the spatially uncalibrated model. Most affected were some sub-basins in the Czech part of the basin where the density of available climate stations was much lower than the German part. Thus, both erroneous precipitation data and systematic flaws in the evapotranspiration module of SWIM could have caused the differences. In order to identify the major error source and to validate the choice of spatial calibration parameters (evapotranspiration and groundwater dynamic corrections), MCMC analyses were made for three Czech areas. Optional precipitation correction had been considered by a third calibration parameter in the MCMC assessment. In two of the three cases, it can be shown that evapotranspiration corrections are preferable as precipitation errors are negligible. In the third case, where the analyses indicate a substantial error in precipitation data, an interpolation problem of the climate data at the edge of the model domain could be found. Hence, the applied method shows its potential to identify specific sources of uncertainty in hydrological modelling.

关键词： Bayesian uncertainty analysis Elbe River basin Error signature Error sources MCMC metropolis algorithm Spatial calibration Spatially distributed hydrological modelling SWIM

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Sparse Locally-Jammed Disk Packings

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ANNALS OF COMBINATORICS 2012年第4期16卷 773-780页

作者： Kahle, Matthew Inst Adv Study Princeton NJ 08540 USA

We construct arbitrarily sparse locally-jammed packings of non-overlapping congruent disks in various finite area regions-in particular, we give constructions for the square, hexagon, and for certain flat tori.

关键词： disk packing hard spheres gas metropolis algorithm

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Syzygies for metropolis base chains

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LINEAR ALGEBRA AND ITS APPLICATIONS 2011年第10期434卷 2176-2186页

作者： Dinwoodie, Ian H. Portland State Univ Fariborz Maseeh Dept Math & Stat Portland OR 97207 USA

It is shown that Markov chains for sampling from combinatorial sets in the form of experimental designs can be made more efficient by using syzygies on gradient vectors. Examples are presented. (C) 2011 Elsevier Inc. ... 详细信息

关键词： Annealing metropolis algorithm Sampling Syzygies

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Fast image reconstruction for Compton camera using stochastic origin ensemble approach

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MEDICAL PHYSICS 2011年第1期38卷 429-438页

作者： Andreyev, Andriy Sitek, Arkadiusz Celler, Anna Univ British Columbia Dept Radiol Vancouver BC V5Z 1M9 Canada Harvard Univ Sch Med Dept Radiol Boston MA 02115 USA Brigham & Womens Hosp Boston MA 02115 USA

Purpose: Compton camera has been proposed as a potential imaging tool in astronomy, industry, homeland security, and medical diagnostics. Due to the inherent geometrical complexity of Compton camera data, image reconstruction of distributed sources can be ineffective and/or time-consuming when using standard techniques such as filtered backprojection or maximum likelihood-expectation maximization (ML-EM). In this article, the authors demonstrate a fast reconstruction of Compton camera data using a novel stochastic origin ensembles (SOE) approach based on Markov chains. Methods: During image reconstruction, the origins of the measured events are randomly assigned to locations on conical surfaces, which are the Compton camera analogs of lines-of-responses in PET. Therefore, the image is defined as an ensemble of origin locations of all possible event origins. During the course of reconstruction, the origins of events are stochastically moved and the acceptance of the new event origin is determined by the predefined acceptance probability, which is proportional to the change in event density. For example, if the event density at the new location is higher than in the previous location, the new position is always accepted. After several iterations, the reconstructed distribution of origins converges to a quasistationary state which can be voxelized and displayed. Results: Comparison with the list-mode ML-EM reveals that the postfiltered SOE algorithm has similar performance in terms of image quality while clearly outperforming ML-EM in relation to reconstruction time. Conclusions: In this study, the authors have implemented and tested a new image reconstruction algorithm for the Compton camera based on the stochastic origin ensembles with Markov chains. The algorithm uses list-mode data, is parallelizable, and can be used for any Compton camera geometry. SOE algorithm clearly outperforms list-mode ML-EM for simple Compton camera geometry in terms of reconstruction time.

关键词： emission tomography Compton camera image reconstruction noise metropolis algorithm

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Monte Carlo simulation of the two-dimensional Potts model using nonextensive statistics

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2011年第23-24期390卷 4203-4209页

作者： Boer, Attila Transilvania Univ Brasov Dept Phys Brasov 500036 Romania

The standard Potts model is investigated in the framework of nonextensive statistical mechanics. We performed Monte Carlo simulations on two-dimensional lattices with linear sizes ranging from 16 to 64 using the metropolis algorithm, where the classical Boltzmann-Gibbs transition probabilities were modified for the nonextensive case. We found that the Potts model undergoes a phase transition in the nonextensive scenario. We established the order of the phase transition and we computed the critical temperature for different values of the Tsallis entropic index. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Potts model Nonextensive statistical mechanics Fluctuations Phase transitions metropolis algorithm

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Stochastic Generalized Method of Moments

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JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS 2011年第3期20卷 714-727页

作者： Yin, Guosheng Ma, Yanyuan Liang, Faming Yuan, Ying Univ Hong Kong Dept Stat & Actuarial Sci Hong Kong Hong Kong Peoples R China Texas A&M Univ Dept Stat College Stn TX 77843 USA Univ Texas MD Anderson Canc Ctr Dept Biostat Unit 1411 Houston TX 77230 USA

The generalized method of moments (GMM) is a very popular estimation and inference procedure based on moment conditions. When likelihood-based methods are difficult to implement, one can often derive various moment conditions and construct the GMM objective function. However, minimization of the objective function in the GMM may be challenging, especially over a large parameter space. Due to the special structure of the GMM, we propose a new sampling-based algorithm, the stochastic GMM sampler, which replaces the multivariate minimization problem by a series of conditional sampling procedures. We develop the theoretical properties of the proposed iterative Monte Carlo method, and demonstrate its superior performance over other GMM estimation procedures in simulation studies. As an illustration, we apply the stochastic GMM sampler to a Medfly life longevity study. Supplemental materials for the article are available online.

关键词： Generalized linear model Gibbs sampling Iterative Monte Carlo Markov chain Monte Carlo metropolis algorithm Moment condition

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