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检索条件"主题词=Expectation-maximization algorithms"

共 270 条记录，以下是231-240 订阅

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Parameter Estimation For Bursty-Intermittent Observations

Parameter Estimation For Bursty-Intermittent Observations

引用

IEEE Signal Processing and Communications Applications (SIU)

作者： Çagatay Candan Elektrik ve Elektronik Mühendisliği Bölümü Orta Doğu Teknik Üniversitesi

ISBN: (数字)9781728172064

ISBN: (纸本)9781728172071

Parameter estimation problem is examined in the setting where the noise power is allowed to change from sample to sample. Parameters of the noise source is assumed to be generated by a Markov chain whose state sequence is not known by the observation system. expectation-maximization algorithm is applied for the estimation of desired parameter with the inclusion of unknown state vector of the Markov chain realization as a latent variable. The suggested scheme can be utilized in applications with bursty noise and/or intermittent signals.

关键词： Signal to noise ratio Markov processes Hidden Markov models Kalman filters Estimation Parameter estimation expectation-maximization algorithms

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Acoustic Model Adaptation Based on Coarse/Fine Training of Transfer Vectors Using Directional Statistics

Acoustic Model Adaptation Based on Coarse/Fine Training of T...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： S. Watanabe A. Nakamura NTT Communication Science Laboratories NTT Corporation Japan

In this paper, we reformulate an adaptation scheme of coarse/fine training (CFT) of transfer vectors in acoustic modeling by using directional statistics. In CFT, the transfer vector is decomposed into a unit direction vector and a scaling factor. By using coarse tied Gaussian class (coarse class) estimation for the unit direction vector, and by using fine tied Gaussian class (fine class) estimation for the scaling factor, we can obtain accurate transfer vectors with a small number of free parameters. Directional statistics is a method for analyzing geometric parameters (e.g. angle and unit vector) using directional data, and is suited for the analysis of the CFT representation. Using directional statistics as a basis, we construct expectation-maximization algorithms for CFT parameters analytically using the maximum likelihood and Bayesian (maximum a posteriori) approaches. In particular, with the Bayesian approach, prior and posterior distributions for unit direction vectors are represented with a von Mises distribution, a representative distribution in directional statistics. Speaker adaptation experiments show that our proposal improves the performance of large vocabulary continuous speech recognition due to the efficient coarse/fine representation of transfer vectors, compared with the conventional transfer vector adaptation

关键词： Adaptation model Statistics Statistical analysis Statistical distributions Bayesian methods expectation-maximization algorithms Algorithm design and analysis Maximum likelihood estimation Proposals Vocabulary

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Image Identification And Restoration In The Subband Domain

Image Identification And Restoration In The Subband Domain

引用

Multidimensional Signal Processing Workshop

作者： J.W. Woods Jaemin Kim Electrical Computer Systems Engineering Rensselaer Polytechnic Institute Troy NY USA

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Object tracking by adaptive modeling

Object tracking by adaptive modeling

引用

IEEE International Conference on Image Processing

作者： A. Rares M.J.T. Reinders Information and Communication Theory Grop Faculty of Information Technology and Systems Delft University of Technnology Netherlands

This paper addresses the problem of object tracking in image sequences. The approach taken is based upon adaptive statistical models. An object selected in a frame by a user is tracked throughout the sequence by using a blob-like description of its features. The object features are continuously updated by using the on-line version of the expectation-maximization algorithm. The proposed object description results in a flexible representation.

关键词： Image restoration Iterative algorithms Image sequences Shape Statistics Frequency Information technology expectation-maximization algorithms Motion pictures Layout

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Noise propagation in SPECT images reconstructed using an iterative maximum-likelihood algorithm

Noise propagation in SPECT images reconstructed using an ite...

引用

IEEE Symposium on Nuclear Science (NSS/MIC)

作者： S.C. Liew C.F. Ku B.H. Hasegawa Department of Physics National University of Singapore Singapore Department of Radiology and Bioengineering Graduate Group University of California San Francisco San Francisco CA USA

Computer simulations were performed to investigate the effects of the photon noise in radionuclide projection data and the uncertainty in the attenuation map on the image noise in attenuation-corrected SPECT (single photon emission computed tomography) images reconstructed using a maximum-likelihood expectation-maximization algorithm. Simulated radionuclide projection data from a head phantom were acquired using a fan-beam geometry consistent with the emission-transmission (ETCT) system being developed at the University of California at San Francisco. The mean ( mu ) of the reconstructed pixel values in the region of interest started to converge after 10 iterations and the standard deviation ( sigma ) increased with the number of iterations. The sigma / mu ratio was found to be inversely proportional to the square root of the total detected counts and proportional to the relative uncertainty in the attenuation maps. These two noise components contributed independently towards the noise in the reconstructed image. In a typical ETCT system employing an X-ray tube for attenuation map acquisition, the uncertainty in the reconstructed radionuclide distribution would be limited mainly by photon noise in the projection data.< >

关键词： Image reconstruction Single photon emission computed tomography Uncertainty Attenuation Computer simulation Maximum likelihood detection expectation-maximization algorithms Computational modeling Solid modeling Head

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Visual learning and recognition of a probabilistic spatio-temporal model of cyclic human locomotion

Visual learning and recognition of a probabilistic spatio-te...

引用

International Conference on Pattern Recognition

作者： M. Peternel A. Leonardis Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia

We present a novel representation of cyclic human locomotion based on a set of spatio-temporal curves of tracked points on the surface of a person. We start by extracting a set of continuous, phase aligned spatio-temporal curves from trajectories of random points tracked over several cycles of locomotion in a monocular video sequence. We analyze a PCA representation of a set of cyclic curves, pointing out properties of the representation which can be used for spatio-temporal alignment in tracking and recognition tasks. We model the curve distribution density by a mixture of Gaussians using expectation-maximization algorithm. For recognition, we use maximum a posteriori estimate combined with linear data adaptation. We tested the algorithms on CMU MoBo database with favourable results for the recognition of people "by walking "from monocular video sequences captured from the side view.

关键词： Humans Video sequences Trajectory Principal component analysis Gaussian distribution expectation-maximization algorithms Maximum a posteriori estimation Testing Databases Legged locomotion

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ROBUST RETINAL IMAGE REGISTRATION USING expectation MAXIMISATION WITH MUTUAL INFORMATION

ROBUST RETINAL IMAGE REGISTRATION USING EXPECTATION MAXIMISA...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Parminder Singh Reel Laurence S. Dooley K. C. P. Wong Anko Borner Department of Communication and Systems The Open University Milton Keynes United Kingdom

ISBN: (纸本)9781479903573

Retinal images (RI) are widely used to diagnose a variety of eye conditions and diseases such as myopia and diabetic retinopathy. They are inherently characterised by having nonuniform illumination and low-contrast homogeneous regions which represent a unique set of challenges for retinal image registration (RIR). This paper investigates using the expectation maximization for principal component analysis based mutual information (EMPCA-MI) algorithm in RIR. It combines spatial features with mutual information to efficiently achieve improved registration performance. Experimental results for mono-modal RI datasets verify that EMPCA-MI together with Powell-Brent optimization affords superior robustness in comparison with existing RIR methods, including the geometrical features method.

关键词： Image registration principal component analysis mutual information expectation-maximization algorithms retinopathy image registration expectation-maximisation algorithm principal components analysis Retinopathy

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Effect of Residual Channel Estimation Errors in Random Access Methods for Satellite Communications

Effect of Residual Channel Estimation Errors in Random Acces...

引用

IEEE Vehicular Technology Conference

作者： Karine Zidane Jerome Lacan Marie-Laure Boucheret Charly Poulliat Mathieu Gineste Damien Roques Caroline Bes Arnaud Deramecourt University of Toulouse ISAE/DMIA & TeSA University of Toulouse IRIT/ENSEEIHT Thales Alenia Space CNES

ISBN: (纸本)9781479980895

In recent random access methods used for satellite communications, collisions between packets are not considered as destructive. In fact, to deal with the collision problem, successive interference cancellation is performed at the receiver. Generally, it is assumed that the receiver has perfect knowledge of the interference. In practice, the interference term is affected by the transmission channel parameters, i.e., channel attenuation, timing offsets, frequency offsets and phase shifts, and needs to be accurately estimated and canceled to avoid performance degradation. In this paper, we study the performance of an enhanced channel estimation technique combining estimation using an autocorrelation based method and the expectation-maximization algorithm integrated in a joint estimation and decoding scheme. We evaluate the effect of residual estimation errors after successive interference cancellation. To validate our experimental results, we compare them to the Cramer-Rao lower bounds for the estimation of channel parameters in case of superimposed signals.

关键词： Satellite communication Network coding Channel estimation expectation-maximization algorithms Cramer Rao Bounds

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A Probabilistic Algorithm for Meg Source Reconstruction

A Probabilistic Algorithm for Meg Source Reconstruction

引用

Sensor Array and Multichannel Signal Processing Workshop

作者： J.M. Zumer H.T. Attias K. Sekihara S.S. Nagarajan Biomagnetic Imaging Lab Department of Radiology University of California San Francisco CA USA Golden Metallic Inc. San Francisco CA USA Department of Systems Design and Engineering Tokyo Metropolitan University Tokyo Japan

We present a novel algorithm for source localization based on probabilistic modeling of stimulus-evoked MEG/EEG data. This algorithm localizes multiple dipoles with the computational complexity equivalent to a single dipole scan, and is therefore more efficient than traditional multidipole fitting procedures. The algorithm assumes that the activity of multiple dipolar sources can be characterized by a linear combination of known temporal basis functions with unknown coefficients. We model the sensor data as arising from activity in each voxel of interest, plus background activity. We estimate temporal basis functions from the data using a probabilistic algorithm called partitioned-factor analysis, previously developed in our lab. We model background activity outside the voxel of interest as an unknown linear mixture of unobserved background factors plus diagonal sensor noise. We use an expectation-maximization algorithm to calculate MAP estimates of unknown basis function coefficients, background mixing matrix, sensor noise covariance and the likelihood of a dipole in each voxel of interest. In simulations, the algorithm is able to accurately localize several simultaneously-active dipoles, at SNRs typical for averaged MEG data. The algorithm performs well even in configurations that include deep sources and highly correlated sources, and thus is superior to MUSIC and beamforming techniques which are sensitive to correlated sources. The algorithm also correctly localizes real somatosensory and auditory evoked fields to the postcentral sulcus and lower bank of the lateral sulcus, respectively

关键词： Brain modeling Background noise Electroencephalography Computational complexity Sensor phenomena and characterization Partitioning algorithms Algorithm design and analysis expectation-maximization algorithms Covariance matrix Multiple signal classification

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New Detectors for Watermarks with Unknown Power Based on Student-t Image Priors

New Detectors for Watermarks with Unknown Power Based on Stu...

引用

IEEE Workshop on Multimedia Signal Processing

作者： Antonis Mairgiotis Giannis Chantas Nikolaos Galatsanos Konstantinos Blekas Yongyi Yang Department of Computer Science University of Ioannina (UoI) Ioannina Greece Department of Electrical and Computer Engineering Illinois Institute of Technology Chicago IL USA

ISBN: (纸本)9781424412730

In this paper we present new detectors for additive watermarks when the power of the watermark is unknown. These detectors are based on modeling the image using student-t statistics. As a result, due to the generative properties of the student-t density function, such models are spatially adaptive and the expectation-maximization algorithm can be used to obtain maximum likelihood estimates of their parameters. Using these image models detectors based on the generalized likelihood ratio and Rao tests are derived for this problem. Numerical experiments are presented that demonstrate the properties of these detectors and compared them with previously proposed detectors.

关键词： Detectors Watermarking Additives Statistics Density functional theory expectation-maximization algorithms Maximum likelihood detection Maximum likelihood estimation Parameter estimation Testing

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