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IEEE International Conference on Multimedia and Expo (ICME)

作者： Zulfiqar Hasan Khan Irene Yu-Hua Gu Tiesheng Wang Andrew Backhouse Department of Signals and Systems Chalmers University of Technology Gothenburg Sweden

We propose a novel tracking scheme that jointly employs point feature correspondences and object appearance similarity. For selecting point correspondences, we use a subset of scale-invariant point features from SIFT that agree with a pre-defined affine transformation. The selected consensus points are then used for pre-selecting candidate regions. For appearance similarity based tracking, we employ an existing anisotropic mean shift, from which the formula for estimating bounding box parameters (width, height, orientation and center) are derived. A switching criterion is utilized to handle the situation where only a small number of point correspondences is found. Experiments and evaluation are performed on tracking moving objects on videos where objects may contain partial occlusions, intersection, deformation and pose changes among other transforms. Our comparisons with two existing methods have shown that the proposed scheme has yielded marked improvement, especially in terms of reducing tracking drifts, of robustness to occlusions, and of tightness and accuracy of tracked bounding box.

关键词： Anisotropic magnetoresistance Parameter estimation Performance evaluation Kernel Noise robustness Tracking expectation-maximization algorithms Bandwidth

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Cooperative Decoding and Channel Estimation for Superimposed Space-Time Interleaved Coded Signals

Cooperative Decoding and Channel Estimation for Superimposed...

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Berthet Visoz Pourmir SUPELEC Department of Telecommunications France FranceTélécom France Department of Telecommunications SUPELEC Gif-sur-Yvette France

This paper addresses the problem of transmitting space-time interleaved coded signals over multiple-input multiple-output (MIMO) block fading channels with intersymbol interference (ISI). At the receiver, we investigate a cooperative approach in which data detection, decoding, and channel estimation are performed separately and iteratively. Computationally efficient methods based on the expectation-maximization (EM) algorithm are developed for multiple path gain estimation

关键词： Channel estimation MIMO Fading Intersymbol interference Iterative decoding AWGN Telecommunications expectation-maximization algorithms Interleaved codes Degradation

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Image reconstruction for positron-emission tomography when anatomical boundaries are known

Image reconstruction for positron-emission tomography when a...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： D.G. Politte D.L. Snyder Washington University USA

Summary form only given, as follows. A method is proposed for improving estimates of radioactivity distributions obtained with PET (positron emission tomography) by utilizing anatomical information derived from MRI (magnetic resonance imaging) or X-ray CT (computered tomography). An algorithm is identified for computing regularized maximum-likelihood (ML) estimates of radioactivity distributions, subject to the constraint that a region of the image with known boundaries contains an unknown but constant intensity of radioactivity. Regularized ML estimates are computed using an expectation-maximization algorithm. A regularization method with spatially varying sieve and resolution kernels is used to prevent artifacts while preserving sharp boundaries where appropriate. The method is evaluated using a simulation and found to produce more accurate estimates than filtered backprojection or the usual ML algorithm. The variance of the constrained estimator is also studied.< >

关键词： Image reconstruction Maximum likelihood estimation Positron emission tomography Magnetic resonance imaging Distributed computing X-ray imaging Computed tomography expectation-maximization algorithms Spatial resolution Kernel

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Hierarchical blur identification

Hierarchical blur identification

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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

A hierarchical blur identification method which is able to identify severe blurs having a relatively large support size is proposed. The method is based on the expectation-maximization algorithm and begins by identifying a low-pass version of the point-spread function (PSF) from a reduced-resolution version of the blurred image. The result is used to initialize the identification of the PSF from a higher-resolution image. By repeating this process, the support size and resolution of the identified PSF are gradually increased, achieving an efficient identification of large PSFs.< >

关键词： expectation-maximization algorithms

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Estimation of articulated motion using kinematically constrained mixture densities

Estimation of articulated motion using kinematically constra...

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IEEE Nonrigid and Articulated Motion Workshop

作者： E.A. Hunter P.H. Kelly R.C. Jain Department of Electrical and Computer Engineering Visual Computing Laboratory University of California San Diego CA USA

We address the problem of articulated posture estimation in its general form. Namely, the recovery of full 3D articulated posture parameters from an uncontrolled scene. Stochastic modeling of low-level segmented image data is unified with models of object kinematic structure through a constrained mixture of observation processes. A modified expectation-maximization algorithm is proposed for this purpose. Early experiments qualitatively demonstrate the efficacy of our approach, and provide a context for integration for more sophisticated image cues.

关键词： Motion estimation Kinematics Layout State estimation Humans Laboratories Stochastic processes Image segmentation expectation-maximization algorithms Image sequences

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Homography-based image mosaicing for automatically removing partial foreground objects

Homography-based image mosaicing for automatically removing ...

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International Conference on Signal Processing and Communication Systems, ICSPCS

作者： Takeaki Iiyoshi Wataru Mitsuhashi Information and Communication Engineering University of Electro-Communications Tokyo Japan

Image mosaicing is an effective means of constructing a single panoramic image from a series of snapshots taken in different viewing angles. However, in the case of congested traffic scenes with a cluttered environment including vehicles or pedestrians, there are severe difficulties in aligning a pair of snapshots. In such cases, some objects would be taken only in one of the image pair, thereby resulting in failure in stitching the pair of images. This paper deals with three types of techniques for performing an image mosaicing: Homography estimation for determining geometrical relationships between the image pair, expectation-maximization algorithm for removing inconsistent overlapping region, and graph cuts for seamless stitching and background estimation. Experimental results indicate that the proposed technique is effective to synthesize a panoramic image from a series of narrow-field-of-view snapshots.

关键词： Pixel Minimization methods Nonlinear distortion Automotive engineering Layout Vehicles expectation-maximization algorithms Robustness Computer vision Iterative algorithms

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Estimating Parameters of a Probabilistic Heterogeneous Block Model via the EM Algorithm

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JOURNAL OF PROBABILITY AND STATISTICS 2015年第1期2015卷 1-14页

作者： Bolla, Marianna Elbanna, Ahmed Budapest Univ Technol & Econ Inst Math Muegyet Rakpart 3 H-1111 Budapest Hungary

We introduce a semiparametric block model for graphs, where the within- and between-cluster edge probabilities are not constants within the blocks but are described by logistic type models, reminiscent of the 50-year-old Rasch model and the newly introduced alpha-beta models. Our purpose is to give a partition of the vertices of an observed graph so that the induced subgraphs and bipartite graphs obey these models, where their strongly interlaced parameters give multiscale evaluation of the vertices at the same time. In this way, a profoundly heterogeneous version of the stochastic block model is built via mixtures of the above submodels, while the parameters are estimated with a special EM iteration.

关键词： RASCH models PARAMETER estimation HETEROGENEOUS catalysis expectation-maximization algorithms PHYSICAL constants STOCHASTIC models

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A Note on the Adaptive LASSO for Zero-Inflated Poisson Regression

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JOURNAL OF PROBABILITY AND STATISTICS 2018年第1期2018卷 1-9页

作者： Banerjee, Prithish Garai, Broti Mallick, Himel Chowdhury, Shrabanti Chatterjee, Saptarshi JP Morgan Chase & Co New York NY USA NBCUniversal New York NY USA Harvard TH Chan Sch Publ Hlth Dept Biostat Boston MA 02115 USA Broad Inst MIT & Harvard Program Med & Populat Genet Cambridge MA 02142 USA Icahn Sch Med Mt Sinai Dept Genet & Genom Sci New York NY 10029 USA Eli Lilly & Co Indianapolis IN 46285 USA

We consider the problem of modelling count data with excess zeros using Zero-Inflated Poisson (ZIP) regression. Recently, various regularization methods have been developed for variable selection in ZIP models. Among these, EM LASSO is a popular method for simultaneous variable selection and parameter estimation. However, EM LASSO suffers from estimation inefficiency and selection inconsistency. To remedy these problems, we propose a set of EM adaptive LASSO methods using a variety of data-adaptive weights. We show theoretically that the new methods are able to identify the true model consistently, and the resulting estimators can be as efficient as oracle. The methods are further evaluated through extensive synthetic experiments and applied to a German health care demand dataset.

关键词： ZERO-inflated probability distribution POISSON regression PARAMETER estimation expectation-maximization algorithms REGULARIZATION parameter

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EP-GIG priors and applications in bayesian sparse learning

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2012年第1期13卷

作者： Zhihua Zhang Shusen Wang Dehua Liu Michael I. Jordan College of Computer Science and Technology Zhejiang University Hangzhou Zhejiang China Computer Science Division and Department of Statistics University of California Berkeley CA

In this paper we propose a novel framework for the construction of sparsity-inducing priors. In particular, we define such priors as a mixture of exponential power distributions with a generalized inverse Gaussian density (EP-GIG). EP-GIG is a variant of generalized hyperbolic distributions, and the special cases include Gaussian scale mixtures and Laplace scale mixtures. Furthermore, Laplace scale mixtures can subserve a Bayesian framework for sparse learning with nonconvex penalization. The densities of EP-GIG can be explicitly expressed. Moreover, the corresponding posterior distribution also follows a generalized inverse Gaussian distribution. We exploit these properties to develop EM algorithms for sparse empirical Bayesian learning. We also show that these algorithms bear an interesting resemblance to iteratively reweighted l 2 or l 1 methods. Finally, we present two extensions for grouped variable selection and logistic regression.

关键词： expectation-maximization algorithms generalized inverse Gaussian distributions iteratively reweighted minimization methods scale mixtures of exponential power distributions sparsity priors

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The EM Algorithm and Related Statistical Models

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Technometrics 2006年第1期48卷 148页

作者： Singh, Ashok K University of Nevada-Las Vegas

Singh reviews The EM Algorithm and Related Statistical Models edited by Michiko Watanabe and Kazunori Yamaguchi.

关键词： expectation-maximization algorithms Nonfiction

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