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

共 270 条记录，以下是131-140 订阅

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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 (ICASSP)

作者： Reel, Parminder Singh Dooley, Laurence S. Wong, K. C. P. Boerner, Anko Open Univ Dept Commun & Syst Milton Keynes MK7 6AA Bucks England German Aerosp Ctr DLR Opt Sensor Syst Berlin Germany

ISBN: (纸本)9781479903566

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 non-uniform 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

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Track, Check, Repeat: An EM Approach to Unsupervised Tracking

Track, Check, Repeat: An EM Approach to Unsupervised Trackin...

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Harley, Adam W. Zuo, Yiming Wen, Jing Mangal, Ayush Potdar, Shubhankar Chaudhry, Ritwick Fragkiadaki, Katerina Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781665445092

We propose an unsupervised method for detecting and tracking moving objects in 3D, in unlabelled RGB-D videos. The method begins with classic handcrafted techniques for segmenting objects using motion cues: we estimate optical flow and camera motion, and conservatively segment regions that appear to be moving independently of the background. Treating these initial segments as pseudo-labels, we learn an ensemble of appearance-based 2D and 3D detectors, under heavy data augmentation. We use this ensemble to detect new instances of the "moving" type, even if they are not moving, and add these as new pseudo-labels. Our method is an expectation-maximization algorithm, where in the expectation step we fire all modules and look for agreement among them, and in the maximization step we re-train the modules to improve this agreement. The constraint of ensemble agreement helps combat contamination of the generated pseudo-labels (during the E step), and data augmentation helps the modules generalize to yetunlabelled data (during the M step). We compare against existing unsupervised object discovery and tracking methods, using challenging videos from CATER and KITTI, and show strong improvements over the state-of-the-art.

关键词： Training Three-dimensional displays Motion segmentation Detectors Pattern recognition Object tracking expectation-maximization algorithms

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Effect of Residual Channel Estimation Errors in Random Access Methods for Satellite Communications 81

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

引用

81st IEEE Vehicular Technology Conference (VTC Spring)

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

ISBN: (纸本)9781479980888

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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MULTIMODAL RETINAL IMAGE REGISTRATION USING A FAST PRINCIPAL COMPONENT ANALYSIS HYBRID-BASED SIMILARITY MEASURE

MULTIMODAL RETINAL IMAGE REGISTRATION USING A FAST PRINCIPAL...

引用

20th IEEE International Conference on Image Processing (ICIP)

作者： Reel, Parminder Singh Dooley, Laurence S. Wong, K. C. P. Boerner, Anko Open Univ Dept Commun & Syst Milton Keynes MK7 6AA Bucks England German Aerosp Ctr DLR Opt Sensor Syst Berlin Germany

ISBN: (纸本)9781479923410

Multimodal retinal images (RI) are extensively used for analysing various eye diseases and conditions such as myopia and diabetic retinopathy. The incorporation of either two or more RI modalities provides complementary structure information in the presence of non-uniform illumination and low-contrast homogeneous regions. It also presents significant challenges for retinal image registration (RIR). This paper investigates how the expectation maximization for Principal Component Analysis with Mutual Information (EMPCA-MI) algorithm can effectively achieve multimodal RIR. This iterative hybrid-based similarity measure combines spatial features with mutual information to provide enhanced registration without recourse to either segmentation or feature extraction. Experimental results for clinical multimodal RI datasets comprising colour fundus and scanning laser ophthalmoscope images confirm EMPCA-MI is able to consistently afford superior numerical and qualitative registration performance compared with existing RIR techniques, such as the bifurcation structures method.

关键词： Image registration ophthalmological image processing principal component analysis mutual information expectation-maximization algorithms

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EFFICIENT IMPLEMENTATION OF THE EM ALGORITHM FOR MAMMOGRAPHIC IMAGE TEXTURE ANALYSIS WITH MULTIVARIATE GAUSSIAN MIXTURES

EFFICIENT IMPLEMENTATION OF THE EM ALGORITHM FOR MAMMOGRAPHI...

引用

IEEE Statistical Signal Processing Workshop (SSP)

作者： Gallego-Ortiz, Nicolas Fernandez-Mc-Cann, David S. Univ Antioquia Dept Elect & Telecommun Engn Sch Engn Medellin Colombia

ISBN: (纸本)9781457705700

In this paper we present an efficient implementation of the EM algorithm for estimating multivariate gaussian mixture model parameters in the context of local-neighborhood image texture analysis. We illustrate its application in a study case of mass detection in mammography, providing a detailed description of a feasible and efficient implementation. Our proposed method overcomes numerical variable underflow problems by means of logarithmic and exponential manipulations and saves computational time using a look up table approach. We reduced computation time to 57.14% with respect to direct computation, achieving numerical conditions for convergence.

关键词： expectation-maximization algorithms Convergence of numerical methods Image texture analysis Mammography Maximum likelihood estimation

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A DIFFUSION-BASED DISTRIBUTED EM ALGORITHM FOR DENSITY ESTIMATION IN WIRELESS SENSOR NETWORKS

A DIFFUSION-BASED DISTRIBUTED EM ALGORITHM FOR DENSITY ESTIM...

引用

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

作者： Silva Pereira, Silvana Pages-Zamora, Alba Lopez-Valcarce, Roberto Univ Politecn Cataluna Barcelona Tech SPCOM Grp E-08028 Barcelona Spain Univ Vigo GPSC Vigo Spain

ISBN: (纸本)9781479903566

Distributed implementations of the expectation-maximization (EM) algorithm reported in literature have been proposed for applications to solve specific problems. In general, a primary requirement to derive a distributed solution is that the structure of the centralized version enables the computation involving global information in a distributed fashion. This paper treats the problem of distributed estimation of Gaussian densities by means of the EM algorithm in wireless sensor networks using diffusion strategies, where the information is gradually diffused across the network for the computation of the global functions. The low-complexity implementation presented here is based on a two time scale operation for information averaging and diffusion. The convergence to a fixed point of the centralized solution has been studied and the appealing results motivates our choice for this model. Numerical examples provided show that the performance of the distributed EM is, in practice, equal to that of the centralized scheme.

关键词： Distributed algorithms expectation-maximization algorithms Gaussian distribution unsupervised learning wireless sensor networks

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Blur identification using the expectation-maximization algorithm

Blur identification using the expectation-maximization algor...

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

作者： R.L. Lagendijk J. Biemond D.E. Boekee Department of Electrical Engineering Information Theory Group Delft University of Technnology Delft Netherlands

The authors present a maximum-likelihood blur identification method, which estimates the required parameters from the observed noisy blurred image itself, and use the expectation-maximization algorithm to solve the resulting complicated problem of optimizing the likelihood function. A priori information about the unknown parameters in the form of initial conditions and parametric image and blur models are incorporated to make the algorithm applicable to realistic blurs and to improve the identification results. Experimental results are presented on a 256 pixel*256 pixel synthetically blurred by a 2-D Gaussian point spread function with various standard deviations. The approach results in a flexible iterative algorithm that is computationally far more efficient than directly optimizing the likelihood function.< >

关键词： expectation-maximization algorithms Image restoration Degradation Iterative algorithms Parameter estimation Optimization methods Maximum likelihood estimation Information theory Filters Flexible structures

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Estimation of cortical multivariate autoregressive models for EEG/MEG using an expectation-maximization algorithm

Estimation of cortical multivariate autoregressive models fo...

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IEEE International Symposium on Biomedical Imaging

作者： Bing Leung Patrick Cheung Barry D. Van Veen Department of Electrical and Computer Engineering University of Wisconsin - Madison 53706 U.S.A. Department of Electrical and Computer Engineering University of Wisconsin - Madison Madison WI USA University of Wisconsin Madison Madison WI US

A new method for estimating multivariate autoregressive (MVAR) models of cortical connectivity from surface EEG or MEG measurements is presented. Conventional approaches to this problem first attempt to solve the inverse problem to estimate cortical signals and then fit an MVAR model to the estimated signals. Our new approach expresses the measured data in tens of a hidden state equation describing MVAR cortical signal evolution and an observation equation that relates the hidden state to the surface measurements. We develop an expectation-maximization (EM) algorithm to find maximum likelihood estimates of the MVAR model parameters. Simulations show that this one-step approach performs significantly better than the conventional two-step approach at estimating the cortical signals and detecting functional connectivity between different cortical regions.

关键词： Brain modeling Electroencephalography expectation-maximization algorithms Equations Surface fitting Inverse problems Maximum likelihood detection Maximum likelihood estimation Signal processing Electric variables measurement

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Automatic threshold tracking of sensor data using expectation maximization algorithm

Automatic threshold tracking of sensor data using Expectatio...

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International Conference on Hybrid Intelligent Systems (HIS)

作者： Arghad Arnaout Bilal Esmael Rudolf K. Fruhwirth Gerhard Thonhauser TDE GmbH Leoben Austria University of Leoben Leoben Austria

In this paper we present a novel method for automatic threshold handling and tracking of sensor data at drilling rigs. A hybrid system for automated drilling operation classification is extended by the expectation maximization algorithm in combination with the Bayes' theorem to find automatically threshold values required by a rule based system used in an automated drilling operations classification system. The streaming data from the rig site is gathered and analyzed, the main clusters in the sensor data are identified and monitored as in a real life case. The first part of the suggested method is based on the expectation maximization algorithm which is used to decompose Gaussian mixture models in the sensor data set. Bayes' theorem is used as a subsequent part to calculate optimal threshold values. The threshold values calculation concept is heavily depending on the likelihood probabilities of each data cluster. The work in this paper not only suggests a solution and analytical method for tracking this kind of thresholds in the sensor data but also verifies how to compute such reliable thresholds in real-time.

关键词： Gaussian distribution Histograms Clustering algorithms Algorithm design and analysis Drilling machines expectation-maximization algorithms Classification algorithms

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Restoration from partially-known blur using an expectation-maximization algorithm

Restoration from partially-known blur using an expectation-m...

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Asilomar Conference on Signals, Systems & Computers

作者： V.Z. Mesarovic N.P. Galatsanos M.N. Wernick Department of Electrical and Computer Engineering Illinois Institute of Technology Chicago IL USA

In this paper we address the problem of image restoration when the point-spread function (PSF) of the imaging process is not known exactly, a situation which arises regularly in practice. The algorithm based on the expectation-maximization (EM) algorithm is proposed which has the capability to identify the unknown statistics of the image and the image-dependent noise while restoring the image. The convergence properties of the resulting estimators are examined.

关键词： expectation-maximization algorithms Image restoration Convergence Covariance matrix Filters Positron emission tomography White noise Image converters Deconvolution Biomedical imaging

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