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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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Direct maximization of the likelihood of a hidden Markov model

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2008年第9期52卷 4147-4160页

作者： Turner, Rolf Univ Auckland Starpath Project Auckland 1 New Zealand Univ New Brunswick Dept Math & Stat Fredericton NB E3B 5A3 Canada

Ever since the introduction of hidden Markov models by Baum and his co-workers, the method of choice for fitting such models has been maximum likelihood via the EM algorithm. In recent years it has been noticed that the gradient and Hessian of the log likelihood of hidden Markov and related models may be calculated in parallel with a filtering process by which the likelihood may be calculated. Various authors have used, or suggested the use of, this idea in order to maximize the likelihood directly, without using the EM algorithm. In this paper we discuss an implementation of such an approach. We have found that a straightforward implementation of Newton's method sometimes works but is unreliable. A form of the Levenberg-Marquardt algorithm appears to provide excellent reliability. Two rather complex examples are given for applying this algorithm to the fitting of hidden Markov models. In the first a better than 6-fold increase in speed over the EM algorithm was achieved. The second example turned out to be problematic (somewhat interestingly) in that the maximum likelihood estimator appears to be inconsistent. Whatever its merit, this estimator is calculated much faster by Levenberg-Marquardt than by EM. We also compared the Levenberg-Marquardt algorithm, applied to the first example, with a generic numerical maximization procedure. The Levenberg-Marquardt algorithm appeared to perform almost three times better than the generic procedure, even when analytic derivatives were provided, and 19 times better when they were not provided. (c) 2008 Elsevier B.V All rights reserved.

关键词： MARKOV processes STOCHASTIC processes HIDDEN Markov models expectation-maximization algorithms

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On the selection of samples for multivariate regression analysis: application to near-infrared (NIR) calibration models for the prediction of pulp yield in Eucalyptus nitens

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CANADIAN JOURNAL OF FOREST RESEARCH 2008年第10期38卷 2626-2634页

作者： Mora, Christian R. Schimleck, Laurence R. Univ Georgia Warnell Sch Forestry & Nat Resources Athens GA 30602 USA Univ Georgia Warnell Sch Forestry & Nat Resources Athens GA 30602 USA

The effects of using reduced calibration sets on the development of near-infrared (NIR) calibration models for the prediction of kraft pulp yield in Eucalyptus nitens (Dean & Maiden) Maiden trees were explored. Three selection techniques based on NIR spectral data (CADEX (computer-aided design of experiments), DUPLEX, and SELECT algorithms) and one selection method based on a measured property (RANKING algorithm) were used for analysis and compared against a model using all data. The effect of using calibration sets of different sizes was also evaluated. All sample-selection methods resulted in models of similar performance compared with the model fitted using all samples. For calibration purposes, RANKING selection resulted in models with the lowest errors of cross-validation, followed by the DUPLEX, CADEX, and SELECT methods. In terms of validation, the SELECT and CADEX methods resulted in lower errors of prediction compared with the DUPLEX and RANKING algorithms. In general, cross-validation and prediction errors decreased as the number of calibration samples increased. These results show that it is possible to obtain adequate NIR calibration models with a reduced number of samples allowing the remaining samples to be used for model validation and that sample selection based on NIR spectral data alone is as successful as selection based on a measured property.

关键词： algorithms READING COMPUTER-aided design COMPUTER-aided engineering DESIGN ALGEBRA ARITHMETIC -- Foundations COMPUTER programming expectation-maximization algorithms

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A two-parameter lifetime distribution with decreasing failure rate

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2008年第8期52卷 3889-3901页

作者： Tahmasbi, Rasool Rezaei, Sadegh Amirkabir Univ Technol Dept Stat Tehran Iran Shahid Chamran Univ Ahvaz Dept Stat Ahvaz Iran

In this paper, a new two-parameter lifetime distribution with decreasing failure rate is introduced. Various properties of the proposed distribution are discussed. The estimation of the parameters attained by the EM algorithm and their asymptotic variances and covariances are obtained. In order to assess the accuracy of the approximation of variances and covariances of the maximum likelihood estimators, simulation studies are performed and experimental results are illustrated based on real data sets. (c) 2007 Elsevier B.V. All rights reserved.

关键词： expectation-maximization algorithms algorithms ESTIMATION theory MATHEMATICAL statistics

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Online learning with hidden Markov models

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NEURAL COMPUTATION 2008年第7期20卷 1706-1716页

作者： Mongillo, Gianluigi Deneve, Sophie Coll France Ecole Normale Super Dept Etud Cognit Grp Neural Theory F-75006 Paris France

We present an online version of the expectation-maximization (EM) algorithm for hidden Markov models (HMMs). The sufficient statistics required for parameters estimation is computed recursively with time, that is, in an online way instead of using the batch forward-backward procedure. This computational scheme is generalized to the case where the model parameters can change with time by introducing a discount factor into the recurrence relations. The resulting algorithm is equivalent to the batch EM algorithm, for appropriate discount factor and scheduling of parameters update. On the other hand, the online algorithm is able to deal with dynamic environments, i.e., when the statistics of the observed data is changing with time. The implications of the online algorithm for probabilistic modeling in neuroscience are briefly discussed.

关键词： expectation-maximization algorithms MARKOV processes NEUROSCIENCES ONLINE algorithms HIDDEN Markov models

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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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Consensus-based distributed expectation-maximization algorithm for density estimation and classification using wireless sensor networks

Consensus-based distributed expectation-maximization algorit...

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

作者： Pedro A. Forero Alfonso Cano Georgios B. Giannakis Department of ECE University of Minnesota Minneapolis MN USA

The present paper develops a decentralized expectation-maximization (EM) algorithm to estimate the parameters of a mixture density model for use in distributed learning tasks performed with data collected at spatially deployed wireless sensors. The E-step in the novel iterative scheme relies on local information available to individual sensors, while during the M-step sensors exchange information only with their one- hop neighbors to reach consensus and eventually percolate the global information needed to estimate the wanted parameters across the wireless sensor network (WSN). Analysis and simulations demonstrate that the resultant consensus-based distributed EM (CB-DEM) algorithm matches well the resource- limited characteristics of WSNs and compares favorably with existing alternatives because it has wider applicability and remains resilient to inter-sensor communication noise.

关键词： expectation-maximization algorithms Wireless sensor networks Parameter estimation Sensor phenomena and characterization Maximum likelihood estimation Local government Gaussian noise Closed-form solution Statistical distributions Additive noise

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3-D Object Recognition Using 2-D Views

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2008年第11期17卷 2236-2255页

作者： Li, Wenjing Bebis, George Bourbakis, Nikolaos G. Univ Nevada Dept Comp Sci & Engn Reno NV 89557 USA Wright State Univ Inst Informat Technol Dayton OH 45435 USA

We consider the problem of recognizing 3-D objects from 2-D images using geometric models and assuming different viewing angles and positions. Our goal is to recognize and localize instances of specific objects (i.e., model-based) in a scene. This is in contrast to category-based object recognition methods where the goal is to search for instances of objects that belong to a certain visual category (e.g., faces or cars). The key contribution of our work is improving 3-D object recognition by integrating Algebraic Functions of Views (AFoVs), a powerful framework for predicting the geometric appearance of an object due to viewpoint changes, with indexing and learning. During training, we compute the space of views that groups of object features can produce under the assumption of 3-D linear transformations, by combining a small number of reference views that contain the object features using AFoVs. Unrealistic views (e.g., due to the assumption of 3-D linear transformations) are eliminated by imposing a pair of rigidity constraints based on knowledge of the transformation between the reference views of the object. To represent the space of views that an object can produce compactly while allowing efficient hypothesis generation during recognition, we propose combining indexing with learning in two stages. In the first stage, we sample the space of views of an object sparsely and represent information about the samples using indexing. In the second stage, we build probabilistic models of shape appearance by sampling the space of views of the object densely and learning the manifold formed by the samples. Learning employs the expectation-maximization (EM) algorithm and takes place in a "universal," lower-dimensional, space computed through Random Projection (RP). During recognition, we extract groups of point features from the scene and we use indexing to retrieve the most feasible model groups that might have produced them (i.e., hypothesis generation). The likelihood

关键词： OPTICAL pattern recognition THREE-dimensional imaging IMAGING systems COMPUTER vision ALGEBRAIC functions expectation-maximization algorithms

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A comparison of clustering fully polarimetric SAR images using SEM algorithm and G0P mixture modelwith different initializations

A comparison of clustering fully polarimetric SAR images usi...

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International Conference on Pattern Recognition

作者： Michelle M. Horta Nelson D. A. Mascarenhas Alejandro C. Frery Physics Institute of São Carlos University of São Paulo Sao Paulo Brazil Computer Department Federal University of São Carlos Sao Paulo Brazil Computer Institute Federal University of Alagoas Maceio Alagoas Brazil

ISBN: (纸本)9781424421749

This paper presents a comparison between two types of initializations for multilook polarimetric SAR image segmentation: a random partition and a sample quantile partition. These are the inputs of a stochastic expectation-maximization algorithm that uses a mixture of G 0 P distributions to describe the data. The parameters are unknown, and estimated by the moments method. The G 0 P law is able to describe different type of targets, like urban areas, vegetation and pasture. The experimental results on real PolSAR data are reported, showing that the use of G 0 P model with quantile partition inicialization provide good segmentation results with few iterations.

关键词： Clustering algorithms Covariance matrix Physics computing Image segmentation Convergence Polarization Speckle Stochastic processes expectation-maximization algorithms Urban areas

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Photon-limited image denoising by inference on multiscale models

Photon-limited image denoising by inference on multiscale mo...

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IEEE International Conference on Image Processing

作者： Stamatios Lefkimmiatis George Papandreou Petros Maragos School of ECE National and Technical University of Athens Athens Greece

We present an improved statistical model of Poisson processes, with applications to photon-limited imaging. We build on previous work, adopting a multiscale representation of the Poisson process in which the ratios of the underlying Poisson intensities (rates) in adjacent scales are modeled as mixtures of conjugate parametric distributions. Our main novel contributions are (1) a rigorous and robust regularized expectation-maximization (EM) algorithm for maximum-likelihood estimation of the rate-ratio density parameters directly from the observed Poisson data (counts); (2) extension of the method to work under a scale-recursive hidden Markov tree model (HMT) which couples the mixture label assignments in consecutive scales, thus modeling inter-scale coefficient dependencies in the vicinity of edges; and (3) exploration of a fully 2-D quad-tree image partitioning, involving Dirichlet-mixture rate-ratio densities, instead of the conventional separable binary image partitioning involving Beta-mixture rate-ratio densities. Experimental intensity estimation results on standard images with artificially simulated Poisson noise and photon-limited images with real shot noise demonstrate the effectiveness of the proposed approach.

关键词： Image denoising Hidden Markov models Parameter estimation Bayesian methods Robustness Gunshot detection systems expectation-maximization algorithms Degradation Fluctuations Pixel

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