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International Conference on Control, Automation, Robotics and Vision

作者： Mahshid Farzinfar Eam Khwang Teoh Zhong Xue School of Electrical Electronic Engineering Nanyang Technological University Singapore The Methodist Hospital Research Institute Weill Medical College of Cornell University Houston TX USA

ISBN: (纸本)9781424478149

According to the level of information provided in images, segmentation techniques can be categorized into two groups. One is region-labeling, which obeys the intensity-based classification methods. Although modeling the tissue intensity is straightforward by applying local statistical methods and spatial dependencies, the results might suffer from noise and incomplete data. The second group of techniques applies active contour models, in which the objective is to find the optimal partition of the image domain using a closed or open curve by using prior constraints on the shape variation. However, estimating optimal curve is intractable due to the incomplete observation data. This paper extends a previously reported joint active contour model for medical image segmentation in a new expectation-maximization (EM) framework, wherein the evolution curve is constrained not only by a shape-based statistical model but also by applying a hidden variable model from the image observation. In this approach, the hidden variable model is defined by the local voxel labeling computed from its likelihood function, depended on the image functions and the prior anatomical knowledge. Comparative results on segmenting putamen and caudate shapes in MR brain images confirmed both robustness and accuracy of the proposed curve evolution algorithm.

关键词： Statistical shape model active contours level set methods expectation-maximization algorithm

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Weather regimes designed for local precipitation modeling: Application to the Mediterranean basin

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JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES 2010年第D12期115卷

作者： Vrac, Mathieu Yiou, Pascal Ctr Etud Saclay Lab Sci Climat & Environm F-91191 Gif Sur Yvette France

Although weather regimes are often used as a primary step in many statistical downscaling processes, they are usually defined solely in terms of atmospheric variables and seldom to maximize their correlation to observed local meteorological phenomena. This paper compares different clustering methods to perform such a task. The correlation clustering model is introduced to define regimes that are well correlated to local-scale precipitation observed on seven French Mediterranean rain gauges. This clustering method is compared to other approaches such as the k-means and "expectation-maximization" (EM) algorithms. The two latter are applied either to the main principal components of large-scale reanalysis data(geopotential height at 500 mbar and sea level pressure) covering the Mediterranean basin or to the canonical variates associated with large scale and resulting from a canonical correlation analysis performed on reanalyses and local precipitation. The weather regimes obtained by the different approaches are compared, with a focus on the "extreme content" captured within the regimes. Then, cost functions are developed to quantify the errors due to misclassification, in terms of local precipitation. The different clustering approaches show different misclassification and costs. EM applied to canonical variates appears as a good compromise between the other approaches, with high discrimination, overall for extreme precipitation, while the precipitation costs due to bad classification are acceptable. This paper provides tools to help the users choose the clustering method to be used according to the expected goal and the use of the weather regimes.

关键词： clustering expectation-maximization algorithm downscaling

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AN EM-algorithm APPROACH FOR THE DESIGN OF ORTHONORMAL BASES ADAPTED TO SPARSE REPRESENTATIONS

AN EM-ALGORITHM APPROACH FOR THE DESIGN OF ORTHONORMAL BASES...

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： A. Dremeau C. Herzet INRIA Centre Rennes - Bretagne Atlantique Campus universitaire de Beaulieu 35000 Rennes France

ISBN: (纸本)9781424442959

In this paper, we consider the problem of dictionary learning for sparse representations. Several algorithms dealing with this problem can be found in the literature. One of them, introduced by Sezer et al. in [1] optimizes a dictionary made up of the union of orthonormal bases. In this paper, we propose a probabilistic interpretation of Sezer's algorithm and suggest a novel optimization procedure based on the EM algorithm. Comparisons of the performance in terms of missed detection rate show a clear superiority of the proposed approach.

关键词： Sparse representations dictionary learning expectation-maximization algorithm expectation-maximisation algorithm sparse representation Alkali Bases Dictionaries as Topic algorithmic approach representations Optimization Procedure detection rate (DR)

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An EM-Based Frequency Offset Estimator for OFDM Systems with Unknown Interference

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2009年第9期8卷 4470-4475页

作者： Sanguinetti, Luca Morelli, Michele Imbarlina, Giuseppe Univ Pisa Dept Informat Engn I-56126 Pisa Italy

We consider an orthogonal frequency-division multiplexing (OFDM) system and address the problem of carrier frequency estimation in the presence of narrowband interference (NBI) with unknown power. This scenario is encountered in emerging spectrum sharing systems, where coexistence of different wireless services over the same frequency band may result into a remarkable co-channel interference, and also in digital subscriber line transmissions as a consequence of the cross-talk phenomenon. A possible solution for frequency recovery in OFDM systems plagued by NBI has recently been derived using the maximum-likelihood criterion. Such scheme exhibits good accuracy, but involves a computationally demanding grid-search over the uncertainty frequency range. In the present work, we derive an alternative method that provides frequency estimates in closed-form by resorting to the expectation-maximization algorithm. This makes it possible to achieve some computational saving while maintaining a remarkable robustness against NBI.

关键词： OFDM carrier frequency offset recovery expectation-maximization algorithm inverse-gamma power distribution

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Rainfall data simulation by hidden Markov model and discrete wavelet transformation

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2009年第7期23卷 863-877页

作者： Jayawardena, A. W. Xu, P. C. Li, W. K. Publ Works Res Inst Int Ctr Water Hazard & Risk Management Tsukuba Ibaraki Japan Chinese Acad Sci Acad Math & Syst Sci Beijing Peoples R China Univ Hong Kong Dept Stat & Actuarial Sci Hong Kong Hong Kong Peoples R China

In many regions, monthly (or bimonthly) rainfall data can be considered as deterministic while daily rainfall data may be treated as random. As a result, deterministic models may not sufficiently fit the daily data because of the strong stochastic nature, while stochastic models may also not reliably fit into daily rainfall time series because of the deterministic nature at the large scale (i.e. coarse scale). Although there are different approaches for simulating daily rainfall, mixing of deterministic and stochastic models (towards possible representation of both deterministic and stochastic properties) has not hitherto been proposed. An attempt is made in this study to simulate daily rainfall data by utilizing discrete wavelet transformation and hidden Markov model. We use a deterministic model to obtain large-scale data, and a stochastic model to simulate the wavelet tree coefficients. The simulated daily rainfall is obtained by inverse transformation. We then compare the accumulated simulated and accumulated observed data from the Chao Phraya Basin in Thailand. Because of the stochastic nature at the small scale, the simulated daily rainfall on a point to point comparison show deviations with the observed data. However the accumulated simulated data do show some level of agreement with the observed data.

关键词： Daily rainfall Discrete wavelet transformation Hidden Markov model expectation-maximization algorithm False nearest neighbours Phase space reconstruction

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A Unified Model-Based Image Analysis Framework for Automated Detection of Precancerous Lesions in Digitized Uterine Cervix Images

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2009年第1期3卷 101-111页

作者： Srinivasan, Yeshwanth Corona, Enrique Nutter, Brian Mitra, Sunanda Bhattacharya, Sonal Ambrado Inc Richardson TX 75080 USA Texas Tech Univ Dept Elect & Comp Engn Lubbock TX 79409 USA

A unified framework for a fully automated diagnostic system for cervical intraepithelial neoplasia (CIN) is proposed. CIN is a detectable and treatable precursor pathology of cancer of the uterine cervix. algorithms based on mathematical morphology, and clustering based on Gaussian mixture modeling (GMM) in a joint color and geometric feature space, are used to segment macro regions. A non-parametric technique, based on the transformation and analysis of the D(R) (distortion-rate) curve is proposed to assess the model order. This technique provides good starting points to infer the GMM parameters via the expectation-maximization (EM) algorithm, reducing the segmentation time and the chances of getting trapped in local optima. The classification of vascular abnormalities in CIN, such as mosaicism and punctations, is modeled as a texture classification problem, and a solution is attempted by characterizing the neighborhood gray-tone dependences and co-occurrence statistics of the textures. The model presented in this paper provides a sequential framework for translating digital images of the cervix into a complete diagnostic tool, with minimal human intervention. In its current form, the research presented in this work may be used to aid physicians to locate abnormalities due to CIN and assess the best areas for a biopsy.

关键词： Cervical cancer cervical intraepithelial neoplasia expectation-maximization algorithm Gaussian mixture model rate-distortion theory

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Cramer-Rao Lower Bound and EM algorithm for Envelope-Based SNR Estimation of Nonconstant Modulus Constellations

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IEEE TRANSACTIONS ON COMMUNICATIONS 2009年第6期57卷 1622-1627页

作者： Gappmair, Wilfried Lopez-Valcarce, Roberto Mosquera, Carlos Graz Univ Technol Inst Commun Networks & Satellite Commun A-8010 Graz Austria Univ Vigo Dept Signal Theory & Commun Vigo Spain

Signal-to-noise ratio (SNR) estimation for linearly modulated signals is addressed in this letter, focusing on envelope-based estimators, which are robust to carrier offsets and phase jitter, and on the challenging case of nonconstant modulus constellations. For comparison purposes, the true Cramer-Rao lower bound is numerically evaluated, obtaining an analytical expression in closed form for the asymptotic case of high SNR values, which quantifies the performance loss with respect to coherent estimation. As the maximum-likelihood algorithm is too complex for practical implementation, an expectation-maximization (EM) approach is proposed, achieving a good tradeoff between complexity and performance for medium-to-high SNRs. Finally, a hybrid scheme based on EM and moments-based estimates is suggested, which performs close to the theoretical limit over a wide SNR range.

关键词： Digital receivers nonconstant modulus signals envelope-based SNR estimation Cramer-Rao lower bound expectation-maximization algorithm

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The beta-binomial convolution model for 2x2 tables with missing cell counts

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STATISTICA NEERLANDICA 2009年第1期63卷 24-42页

作者： Eisinga, Rob Radboud Univ Nijmegen Dept Social Sci Res Methods NL-6500 HE Nijmegen Netherlands

This paper considers the beta-binomial convolution model for the analysis of 2x2 tables with missing cell counts. We discuss maximum-likelihood (ML) parameter estimation using the expectation-maximization algorithm and study information loss relative to complete data estimators. We also examine bias of the ML estimators of the beta-binomial convolution. The results are illustrated by two example applications.

关键词： ecological inference beta-binomial distribution convolution likelihood expectation-maximization algorithm missing information principle Fisher information loss bias correction 2x2 table

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Adaptive Iterative Detectors for Phase-Uncertain Channels via Variational Bounding

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IEEE TRANSACTIONS ON COMMUNICATIONS 2009年第3期57卷 716-725页

作者： Nissila, Mauri Pasupathy, Subbarayan Nokia FIN-90570 Oulu Finland Univ Toronto Dept Elect & Comp Engn Toronto ON M5S 3G4 Canada

The problem of iterative detection/decoding of data symbols transmitted over an additive white Gaussian noise (AWGN) channel in the presence of phase uncertainty is addressed in this paper. By modelling the phase uncertainty either as an unknown deterministic variable/process or random variable/process with a known a priori probability density function, a number of non-Bayesian and Bayesian detection algorithms with various amount of suboptimality have been proposed in the literature to solve the problem. In this paper, a new set of suboptimal iterative detection algorithms is obtained by utilizing the variational bounding technique. Especially, applying the generic variational Bayesian (VB) framework, efficient iterative joint estimation and detection/decoding schemes are derived for the constant phase model as well as for the dynamic phase model. In addition, the relation of the VB-based approach to the optimal noncoherent receiver as well as to the classical approach via the expectation-maximization (EM) algorithm is provided. Performance of the proposed detectors in the presence of a strong dynamic phase noise is compared to the performance of the existing detectors. Furthermore, an incremental scheduling of the VB (or EM) algorithm is shown to reduce the overall complexity of the receiver.

关键词： Adaptive iterative detection variational Bayesian algorithms expectation-maximization algorithm Bayesian estimation

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Development of a Censored Robust Design Model for Time-Oriented Quality Characteristics

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2009年第2期25卷 181-197页

作者： Shaibu, A. -B. Cho, Byung Rae Kovach, Jami Univ Houston Dept Informat & Logist Technol Houston TX 77204 USA Clemson Univ Dept Ind Engn Adv Qual Engn Lab Clemson SC 29634 USA

Robust design (RD) techniques, which are based on the concept of building quality into products or processes, are increasingly popular in industry primarily because of their practicality. Traditional RD principles have often been applied to situations in which the quality characteristics of interest are time-insensitive. However, when time-oriented quality characteristics are studied, censored data often occur. As a result, current RD models reported in the literature may not be effective in finding solutions based on such data. To address such practical needs, this paper develops a censored RD model. We also propose an estimation method that is closely related to the expectation-maximization algorithm and compare it with the method of maximum likelihood estimation via a numerical example. Model validation is conducted, and comparative studies are discussed for model verification. Copyright (C) 2008 John Wiley & Sons, Ltd.

关键词： robust design time-oriented quality characteristics lifetime studies censored data expectation-maximization algorithm method of maximum likelihood estimation

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