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347,247
Fast Algorithms for Phase DiversityBased Blind Deconvolution
, 1998
"... Phase diversity is a technique for obtaining estimates of both the object and the phase, by exploiting the simultaneous collection of two (or more) shortexposure optical images, one of which has been formed by further blurring the conventional image in some known fashion. This paper concerns a fast ..."
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Cited by 26 (5 self)
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fast computational algorithm based upon a regularized variant of the GaussNewton optimization method for phase diversitybased estimation when a Gaussian likelihood fittodata criterion is applied. Simulation studies are provided to demonstrate that the method is remarkably robust and numerically
Unsupervised learning of finite mixture models
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 2002
"... This paper proposes an unsupervised algorithm for learning a finite mixture model from multivariate data. The adjective ªunsupervisedº is justified by two properties of the algorithm: 1) it is capable of selecting the number of components and 2) unlike the standard expectationmaximization (EM) alg ..."
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Cited by 419 (22 self)
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) algorithm, it does not require careful initialization. The proposed method also avoids another drawback of EM for mixture fitting: the possibility of convergence toward a singular estimate at the boundary of the parameter space. The novelty of our approach is that we do not use a model selection criterion
The minimum description length principle in coding and modeling
 IEEE TRANS. INFORM. THEORY
, 1998
"... We review the principles of Minimum Description Length and Stochastic Complexity as used in data compression and statistical modeling. Stochastic complexity is formulated as the solution to optimum universal coding problems extending Shannon’s basic source coding theorem. The normalized maximized ..."
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Cited by 395 (18 self)
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likelihood, mixture, and predictive codings are each shown to achieve the stochastic complexity to within asymptotically vanishing terms. We assess the performance of the minimum description length criterion both from the vantage point of quality of data compression and accuracy of statistical inference
Speaker, Environment And Channel Change Detection And Clustering Via The Bayesian Information Criterion
, 1998
"... In this paper, we are interested in detecting changes in speaker identity, environmental condition and channel condition; we call this the problem of acoustic change detection. The input audio stream can be modeled as a Gaussian process in the cepstral space. We present a maximum likelihood approach ..."
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Cited by 271 (2 self)
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In this paper, we are interested in detecting changes in speaker identity, environmental condition and channel condition; we call this the problem of acoustic change detection. The input audio stream can be modeled as a Gaussian process in the cepstral space. We present a maximum likelihood
Generalized Marginal Likelihood for Gaussian Mixtures
 LSS Internal Report GPI94 01
, 1994
"... The dominant approach in BernoulliGaussian myopic deconvolution consists in the joint maximization of a single Generalized Likelihood with respect to the input signal and the hyperparameters. The aim of this correspondence is to assess the theoretical properties of a related Generalized Marginal ..."
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Cited by 1 (1 self)
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Marginal Likelihood criterion in a simplified framework where the filter is reduced to identity. Then the output is a mixture of Gaussian populations. Under a single reasonable assumption we prove that the maximum generalized marginal likelihood estimator always converge asymptotically. Then numerical
A scheme for robust distributed sensor fusion based on average consensus
 PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON INFORMATION PROCESSING IN SENSOR NETWORKS (IPSN
, 2005
"... We consider a network of distributed sensors, where each sensor takes a linear measurement of some unknown parameters, corrupted by independent Gaussian noises. We propose a simple distributed iterative scheme, based on distributed average consensus in the network, to compute the maximumlikelihoo ..."
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Cited by 255 (3 self)
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We consider a network of distributed sensors, where each sensor takes a linear measurement of some unknown parameters, corrupted by independent Gaussian noises. We propose a simple distributed iterative scheme, based on distributed average consensus in the network, to compute the maximumlikelihood
The nonGaussianity of the cosmic shear likelihood or
, 901
"... Aims. We study the validity of the approximation of a Gaussian cosmic shear likelihood. We estimate the true likelihood for a fiducial cosmological model from a large set of raytracing simulations and investigate the impact of nonGaussianity on cosmological parameter estimation. We investigate how ..."
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Cited by 1 (0 self)
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dimensional distributions. Results. We find that the cosmic shear likelihood is significantly nonGaussian. This leads to both a shift of the maximum of the posterior distribution and a significantly smaller credible region compared to the Gaussian case. We reanalyse the CDFS cosmic shear data using the nonGaussian
A Bayesian Approach to Digital Matting
, 2001
"... This paper proposes a new Bayesian framework for solving the matting problem, i.e. extracting a foreground element from a background image by estimating an opacity for each pixel of the foreground element. Our approach models both the foreground and background color distributions with spatiallyvaryi ..."
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Cited by 236 (3 self)
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with spatiallyvarying sets of Gaussians, and assumes a fractional blending of the foreground and background colors to produce the final output. It then uses a maximumlikelihood criterion to estimate the optimal opacity, foreground and background simultaneously. In addition to providing a principled approach
Results 1  10
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347,247