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Combining generative and discriminative models in a framework for articulated pose estimation

INTERNATIONAL JOURNAL OF COMPUTER VISION 2006年第3期67卷 251-276页

作者： Rosales, Romer Sclaroff, Stan MIT Comp Sci & Artificial Intelligence Lab Cambridge MA 02139 USA Boston Univ Dept Comp Sci Image & Video Comp Grp Boston MA 02215 USA

We develop a method for the estimation of articulated pose, such as that of the human body or the human hand, from a single (monocular) image. Pose estimation is formulated as a statistical inference problem, where the goal is to find a posterior probability distribution over poses as well as a maximum a posteriori (MAP) estimate. The method combines two modeling approaches, one discriminative and the other generative. The discriminative model consists of a set of mapping functions that are constructed automatically from a labeled training set of body poses and their respective image features. The discriminative formulation allows for modeling ambiguous, one-to-many mappings (through the use of multi-modal distributions) that may yield multiple valid articulated pose hypotheses from a single image. The generative model is defined in terms of a computer graphics rendering of poses. While the generative model offers an accurate way to relate observed (image features) and hidden (body pose) random variables, it is difficult to use it directly in pose estimation, since inference is computationally intractable. In contrast, inference with the discriminative model is tractable, but considerably less accurate for the problem of interest. A combined discriminative/generative formulation is derived that leverages the complimentary strengths of both models in a principled framework for articulated pose inference. Two efficient MAP pose estimation algorithms are derived from this formulation;the first is deterministic and the second non-deterministic. Performance of the framework is quantitatively evaluated in estimating articulated pose of both the human hand and human body.

关键词： human body pose hand pose nonrigid and articulated pose estimation statistical inference generative and discriminative models mixture models expectation maximization algorithm

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Stochastic feature compensation methods for speaker verification in noisy environments

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APPLIED SOFT COMPUTING 2014年 19卷 198-214页

作者： Sarkar, Sourjya Rao, K. Sreenivasa Indian Inst Technol Sch Informat Technol Kharagpur 721302 W Bengal India

This paper explores the significance of stereo-based stochastic feature compensation (SFC) methods for robust speaker verification (SV) in mismatched training and test environments. Gaussian Mixture Model (GMM)-based SFC methods developed in past has been solely restricted for speech recognition tasks. Application of these algorithms in a SV framework for background noise compensation is proposed in this paper. A priori knowledge about the test environment and availability of stereo training data is assumed. During the training phase, Mel frequency cepstral coefficient (MFCC) features extracted from a speaker's noisy and clean speech utterance (stereo data) are used to build front end GMMs. During the evaluation phase, noisy test utterances are transformed on the basis of a minimum mean squared error (MMSE) or maximum likelihood (MLE) estimate, using the target speaker GMMs. Experiments conducted on the NIST-2003-SRE database with clean speech utterances artificially degraded with different types of additive noises reveal that the proposed SV systems strictly outperform baseline SV systems in mismatched conditions across all noisy background environments. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Speaker verification Noisy environment Minimum mean squared error Maximum likelihood estimate expectation maximization algorithm Gaussian Mixture Models

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An EM algorithm for dynamic estimation of interfacial boundary in stratified flow of immiscible liquids using EIT

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FLOW MEASUREMENT AND INSTRUMENTATION 2011年第6期22卷 517-528页

作者： Khambampati, Anil Kumar Kim, Sin Kim, Kyung Youn Jeju Natl Univ Dept Elect Engn Cheju 690756 South Korea Jeju Natl Univ Appl Radiol Sci Res Inst Cheju 690756 South Korea Jeju Natl Univ Dept Nucl & Energy Engn Cheju 690756 South Korea

Estimation of interfacial boundary between two immiscible liquids in two-phase flows through pipe line provides information about the flow characteristics and thus can aid in design and monitoring of the flow process. The interfacial boundary can be represented in several ways, one such method is the front point approach. Front points describe the location and the shape of the interfacial boundary separating the immiscible liquids. During the flow process, due to fluctuations the interfacial boundary and so the front points which describe the boundary changes with time. The time-varying interfacial boundary can be estimated using dynamic inverse algorithms based on Kalman filter. However, algorithms based on Kalman filter require complete knowledge of model parameters (initial states, state transition matrix, and noise covariance matrices) for implementation. In processes involving complex flow pattern such as two-phase flows, it is difficult to represent the model parameters in a prior form. This uncertainty in model parameters causes suboptimal performance of the Kalman type filters. In this paper, we employ expectation maximization algorithm (EM) to estimate model parameters along with the interfacial boundary using electrical impedance tomography (EIT). The estimation of model parameters reduces the modeling uncertainty and thus results in improving the tracking of interfacial boundary. Numerical and experimental studies are performed to validate the performance of the proposed method. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Electrical impedance tomography Immiscible liquids Dynamic estimation Kalman filter expectation maximization algorithm

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Estimation and diagnostic tools in reparameterized slashed Rayleigh regression model. An application to chemical data

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2020年 207卷 104189-104189页

作者： Gallardo, Diego I. Gomez-Deniz, Emilio Leao, Jeremias Gomez, Hector W. Univ Atacama Fac Ingn Dept Matemat Copiapo Chile Univ Las Palmas Gran Canaria Dept Quantitat Methods Econ Las Palmas Gran Canaria Spain Univ Las Palmas Gran Canaria TiDES Inst Las Palmas Gran Canaria Spain Univ Fed Amazonas Dept Estatist Manaus AM Brazil Univ Antofagasta Fac Ciencias Basicas Dept Matemat Antofagasta Chile

In this paper, we introduce a regression model where the response variable is reparameterized slashed Rayleigh (RSR) distributed and which is indexed by mean and precision parameters. The proposed regression model is useful for situations where the variable of interest is continuous and restricted to the positive real line and is related to other variables through the mean and precision parameters. In addition, the RSR model has properties that its competitor distributions of the exponential family do not have. Estimation is performed by expectation maximization (EM) and extensions. Furthermore, we discuss residuals and influence diagnostic tools. Finally, we also carry out two applications to real-world data that demonstrate the usefulness of the proposed model.

关键词： expectation maximization algorithm Linear regression Reparameterized Rayleigh distribution Monte Carlo simulation

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Cerebrovascular segmentation from TOF using stochastic models

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MEDICAL IMAGE ANALYSIS 2006年第1期10卷 2-18页

作者： Hassouna, MS Farag, AA Hushek, S Moriarty, T Univ Louisville Comp Vis & Image Proc Lab Louisville KY 40292 USA Norton Hosp iMRI Dept Louisville KY 40202 USA Univ Louisville Dept Neurol Surg Louisville KY 40292 USA

In this paper, we present an automatic statistical approach for extracting 3D blood vessels from time-of-flight (TOF) magnetic resonance angiography (MRA) data. The voxels of the dataset are classified as either blood vessels or background noise. The observed volume data is modeled by two stochastic processes. The low level process characterizes the intensity distribution of the data, while the high level process characterizes their statistical dependence among neighboring voxels. The low level process of the background signal is modeled by a finite mixture of one Rayleigh and two normal distributions, while the blood vessels are modeled by one normal distribution. The parameters of the low level process are estimated using the expectation maximization (EM) algorithm. Since the convergence of the EM is sensitive to the initial estimate of the model parameters, an automatic method for parameter initialization, based on histogram analysis, is provided. To improve the quality of segmentation achieved by the proposed low level model especially in the regions of significantly vascular signal loss, the high level process is modeled as a Markov random field (MRF). Since MRF is sensitive to edges and the intracranial vessels represent roughly 5% of the intracranial volume, 2D MRF will destroy most of the small and medium sized vessels. Therefore, to reduce this limitation, we employed 3D MRF, whose parameters are estimated using the maximum pseudo likelihood estimator (MPLE), which converges to the true likelihood under large lattice. Our proposed model exhibits a good fit to the clinical data and is extensively tested on different synthetic vessel phantoms and several 2D/3D TOF datasets acquired from two different MRI scanners. Experimental results showed that the proposed model provides good quality of segmentation and is capable of delineating vessels down to 3 voxel diameters. (c) 2005 Elsevier B.V. All rights reserved.

关键词： magnetic resonance angiography time-of-flight expectation maximization algorithm Markov random fields statistical segmentation

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Individualized survival predictions using state space model with longitudinal and survival data

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JOURNAL OF THE ROYAL SOCIETY INTERFACE 2024年第216期21卷 20230682-20230682页

作者： Cauchi, Mark Mills, Andrew R. Lawrie, Allan Kiely, David G. Kadirkamanathan, Visakan Univ Sheffield Dept Automatic Control & Syst Engn Mappin St Sheffield S1 3JD England Imperial Coll London Natl Heart & Lung Inst Dovehouse St London SW3 6LY England Univ Sheffield Royal Hallamshire Hosp Sheffield NIHR Biomed Res Ctr Sheffield Sheffield Pulm Vasc Dis Unit Beech Hill Rd Sheffield S10 2RX England Univ Sheffield Dept Clin Med Beech Hill Rd Sheffield S10 2RX England

Monitoring disease progression often involves tracking biomarker measurements over time. Joint models (JMs) for longitudinal and survival data provide a framework to explore the relationship between time-varying biomarkers and patients' event outcomes, offering the potential for personalized survival predictions. In this article, we introduce the linear state space dynamic survival model for handling longitudinal and survival data. This model enhances the traditional linear Gaussian state space model by including survival data. It differs from the conventional JMs by offering an alternative interpretation via differential or difference equations, eliminating the need for creating a design matrix. To showcase the model's effectiveness, we conduct a simulation case study, emphasizing its performance under conditions of limited observed measurements. We also apply the proposed model to a dataset of pulmonary arterial hypertension patients, demonstrating its potential for enhanced survival predictions when compared with conventional risk scores.

关键词： expectation maximization algorithm joint model longitudinal data pulmonary arterial hypertension state space model survival data

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Deep Unfolding-Based Joint Beamforming and Detection Design for Ambient Backscatter Communications With IRS

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IEEE COMMUNICATIONS LETTERS 2023年第4期27卷 1145-1149页

作者： Wang, Ze Xu, Hongbo Wang, Ji Liu, Wanxian He, Xiuli Zhou, Aizhi Cent China Normal Univ Dept Elect & Informat Engn Wuhan 430079 Peoples R China

In this letter, we investigate a novel ambient backscatter communication (AmBC) system in which an intelligent reflecting surface (IRS) acts as a passive transmitter to communicate with a reader. We are interested in the joint design of the IRS reflecting beamforming and symbol detection to minimize the bit error rate (BER). However, the problem is challenging to be solved optimally, since the BER is related to the clustering-based detector without a concrete close-form expression and the IRS reflecting unit modulus constraint is non-convex. To solve this issue, we propose a novel deep unfolding neural network (DUNN) combining data-driven and model-driven for passive reflecting beamforming design and symbol detection, which is learned to approximate the BER model from the training samples and the unit modulus constraint is satisfied by treating the optimization variables as network parameters. Numerical results demonstrate that the proposed scheme has superior performance of detection.

关键词： Symbols Radio frequency Backscatter Optimization Array signal processing Training Neural networks Ambient backscatter communications intelligent reconfigurable surface deep unfolding symbol detection expectation maximization algorithm

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Analysis of E. coli promoter recognition problem in dinucleotide feature space

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BIOINFORMATICS 2007年第5期23卷 582-588页

作者： Rani, T. Sobha Bhavani, S. Durga Bapi, Raju S. Univ Hyderabad Dept Comp & Informat Sci Computat Intelligence Lab Hyderabad 500046 Andhra Pradesh India

Motivation: Patterns in the promoter sequences within a species are known to be conserved but there exist many exceptions to this rule which makes the promoter recognition a complex problem. Although many complex feature extraction schemes coupled with several classifiers have been proposed for promoter recognition in the current literature, the problem is still open. Results: A dinucleotide global feature extraction method is proposed for the recognition of sigma-70 promoters in Escherichia coli in this article. The positive data set consists of sigma-70 promoters with known transcription starting points which are part of regulonDB and promec databases. Four different kinds of negative data sets are considered, two of them biological sets (Gordon et al., 2003) and the other two synthetic data sets. Our results reveal that a single-layer perceptron using dinucleotide features is able to achieve an accuracy of 80% against a background of biological non-promoters and 96% for random data sets. A scheme for locating the promoter regions in a given genome sequence is proposed. A deeper analysis of the data set shows that there is a bifurcation of the data set into two distinct classes, a majority class and a minority class. Our results point out that majority class constituting the majority promoter and the majority non-promoter signal is linearly separable. Also the minority class is linearly separable. We further show that the feature extraction and classification methods proposed in the paper are generic enough to be applied to the more complex problem of eucaryotic promoter recognition. We present Drosophila promoter recognition as a case study. Availability: http://202.41.85.117/htmfiles/faculty/tsr/*** Contact: tsrcs@***.

关键词： expectation maximization algorithm NEURAL NETWORKS BINDING SITES SEQUENCES IDENTIFICATION TRANSCRIPTION STRENGTHS REGIONS

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Robust inference for mixed censored and binary response models with missing covariates

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STATISTICAL METHODS IN MEDICAL RESEARCH 2016年第5期25卷 1836-1853页

作者： Sarkar, Angshuman Das, Kalyan Sinha, Sanjoy K. Novartis Healthcare Pvt Ltd Hyderabad Andhra Pradesh India Univ Calcutta Dept Stat Kolkata W Bengal India Carleton Univ Sch Math & Stat Ottawa ON K1S 5B6 Canada

In biomedical and epidemiological studies, often outcomes obtained are of mixed discrete and continuous in nature. Furthermore, due to some technical inconvenience or else, continuous responses are censored and also a few covariates cease to be observed completely. In this paper, we develop a model to tackle these complex situations. Our methodology is developed in a more general framework and provides a full-scale robust analysis of such complex models. The proposed robust maximum likelihood estimators of the model parameters are resistant to potential outliers in the data. We discuss the asymptotic properties of the robust estimators. To avoid computational difficulties involving irreducibly high-dimensional integrals, we propose a Monte Carlo method based on the Metropolis algorithm for approximating the robust maximum likelihood estimators. We study the empirical properties of these estimators in simulations. We also illustrate the proposed robust method using clustered data on blood sugar content from a clinical trial of individuals who were investigated for diabetes.

关键词： binary model censored regression model expectation maximization algorithm metropolis algorithm missing data robust estimation

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Estimating Multilevel Models on Data Streams

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PSYCHOMETRIKA 2019年第1期84卷 41-64页

作者： Ippel, L. Kaptein, M. C. Vermunt, J. K. Maastricht Univ Maastricht Netherlands Tilburg Univ Tilburg Netherlands

Social scientists are often faced with data that have a nested structure: pupils are nested within schools, employees are nested within companies, or repeated measurements are nested within individuals. Nested data are typically analyzed using multilevel models. However, when data sets are extremely large or when new data continuously augment the data set, estimating multilevel models can be challenging: the current algorithms used to fit multilevel models repeatedly revisit all data points and end up consuming much time and computer memory. This is especially troublesome when predictions are needed in real time and observations keep streaming in. We address this problem by introducing the Streaming expectation maximization Approximation (SEMA) algorithm for fitting multilevel models online (or row-by-row). In an extensive simulation study, we demonstrate the performance of SEMA compared to traditional methods of fitting multilevel models. Next, SEMA is used to analyze an empirical data stream. The accuracy of SEMA is competitive to current state-of-the-art methods while being orders of magnitude faster.

关键词： Data streams expectation maximization algorithm multilevel models machine (online) learning SEMA nested data

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