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15th IEEE International Conference on Image Processing (ICIP 2008)

作者： Lefkimmiatis, Stamatios Papandreou, George Maragos, Petros Natl Tech Univ Athens Sch ECE GR-15773 Athens Greece

ISBN: (纸本)9781424417650

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： Photon-Limited Imaging Poisson Bayesian inference Hidden Markov tree expectation-maximization algorithm

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Predicting the Loss Given Default Distribution with the Zero-Inflated Censored Beta-Mixture Regression that Allows Probability Masses and Bimodality

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JOURNAL OF FINANCIAL SERVICES RESEARCH 2021年第3期59卷 143-172页

作者： Hwang, Ruey-Ching Chu, Chih-Kang Yu, Kaizhi Natl Dong Hwa Univ Dept Finance Hualien Taiwan Natl Dong Hwa Univ Dept Appl Math Hualien Taiwan Southwestern Univ Finance & Econ Sch Stat Chengdu Peoples R China

We propose a new procedure to predict the loss given default (LGD) distribution. Studies find empirical evidence that LGD values have a high concentration at the endpoint 0. Thus, we first use a logistic regression to determine the probability that the LGD value of a defaulted debt equals zero. Further, studies find empirical evidence that positive LGD values have a low concentration at the endpoint 1 and a bimodal distribution on the interval (0,1). Therefore, we use a right-tailed censored beta-mixture regression to model the distribution of positive LGD data. To implement the proposed procedure, we collect 5554 defaulted debts from Moody's Default and Recovery Database and apply an expectation-maximization algorithm to estimate the LGD distribution. Using each of the k-fold cross-validation technique and the expanding rolling window approach, our empirical results confirm that the new procedure has better and more robust out-of-sample performance than its alternatives because it yields more accurate predictions of the LGD distribution.

关键词： Conditional distribution expectation-maximization algorithm Logistic regression Loss given default Right-tailed censored beta-mixture regression Zero-inflated model

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Adaptive Wiener filter based on Gaussian mixture model for denoising chest X-ray CT image

Adaptive Wiener filter based on Gaussian mixture model for d...

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Annual Conference on the Society-of-Instrument-and-Control-Engineers

作者： Tabuchi, Motohiro Yamane, Nobumoto Morikawa, Yoshitaka Okayama Univ Grad Sch Nat Sci & Technol Okayama 7008530 Japan

ISBN: (纸本)9784907764289

Because the X-ray CT imaging has high spatial resolution, it becomes more important in diagnostic imaging. However the techniques of low dose imaging at X-ray mass examination or thin slice imaging provide degraded CT images by noise. The CT images have specific noise, called streak artifact. In this paper, we apply an adaptive Wiener filter (AWF) based on the Gaussian mixture distribution model (GMM), proposed previously to reduce Gaussian white noise. Simulation results show that a new AWF-GMM designed using high dose (original) CT image and low dose (observed) CT image pairs of chest phantom for training image set provides high restoration ability.

关键词： Gaussian mixture distribution model expectation-maximization algorithm Maximum a posteriori probability phantom adaptive Wiener filter

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Applying Training Hidden Features to Joint Curve Evolution for Brain MRI Segmentation

Applying Training Hidden Features to Joint Curve Evolution f...

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11th International Conference on Control, Automation, Robotics and Vision (ICARCV 2010)

作者： Farzinfar, Mahshid Teoh, Eam Khwang Xue, Zhong Nanyang Technol Univ Sch Elect Elect Engn Singapore 639798 Singapore Cornell Univ Methodist Hosp Res Inst Weill Med Coll Houston TX USA

ISBN: (纸本)9781424478132

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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A NOVEL APPROACH FOR SYNTHETIC REDUCED NEAREST-NEIGHBOR LEVERAGING NEURAL NETWORKS 21

A NOVEL APPROACH FOR SYNTHETIC REDUCED NEAREST-NEIGHBOR LEVE...

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21st IEEE International Conference on Machine Learning and Applications (IEEE ICMLA)

作者： Alizadeh, Azar Tavallali, Pooya Behzadan, Vahid Ranganath, Aditya Singhal, Mukesh Univ Calif Merced Elect Engn & Comp Sci Merced CA 95348 USA Univ New Haven SAIL Lab West Haven CT 06516 USA

ISBN: (纸本)9781665462839

Synthetic Reduced Nearest Neighbor is a nearest neighbor model that is constrained on synthetic samples (i.e., prototypes). The body of work on such models includes proposals for improving the interpretability and optimization of SRNN models using expectation maximization. Motivated by the promise of this paradigm, we propose a novel expectation maximization approach for Synthetic Reduced Nearest Neighbors leveraging neural networks. Furthermore, we compare the performance of our proposed technique to classical state-of-the-art machine learning methods such as random forest and ensemble models. The empirical results demonstrate the advantages of using neural networks in lieu of an expectation maximization algorithm.

关键词： Synthetic Reduced Nearest Neighbor Machine learning Regression expectation-maximization algorithm

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CUDA-based Parallel Implementation of IBM Word Alignment algorithm for Statistical Machine Translation 17

CUDA-based Parallel Implementation of IBM Word Alignment Alg...

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17th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)

作者： Jing, Si-Yuan Yan, Gao-Rong Chen, Xing-Yuan Jin, Peng Guo, Zhao-Yi Leshan Normal Univ Sch Comp Sci Leshan Peoples R China Leshan Normal Univ Sch Foreign Language Leshan Peoples R China

ISBN: (纸本)9781509050819

Word alignment is a basic task in natural language processing and it usually serves as the starting point when building a modern statistical machine translation system. However, the state-of-art parallel algorithm for word alignment is still time-consuming. In this work, we explore a parallel implementation of word alignment algorithm on Graphics Processor Unit (GPU), which has been widely available in the field of high performance computing. We use the Compute Unified Device Architecture (CUDA) programming model to re-implement a state-of-the-art word alignment algorithm, called IBM expectation-maximization (EM) algorithm. A Tesla K40M card with 2880 cores is used for experiments and execution times obtained with the proposed algorithm are compared with a sequential algorithm and a multi-threads algorithm on an IBM X3850 server, which has two Intel Xeon E7 CPUs (2.0GHz * 10 cores). The best experimental results show a 16.8-fold speedup compared to the multi-threads algorithm and a 234.7-fold speedup compared to the sequential algorithm.

关键词： Word Alignment GPU Parallel Computation expectation-maximization algorithm CUDA

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Estimation of a certain class of chaotic signals:: An EM-based approach

Estimation of a certain class of chaotic signals:: An EM-bas...

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Pantaleón, C Vielva, L Luengo, D Santamaría, I Univ Cantabria Dpto Ing Comunicaciones ETSII & Telecom E-39005 Santander Spain

ISBN: (纸本)0780374029

Maximum-likelihood estimation of chaotic signals generated by iterating piecewise-linear maps on the unit interval exhibits an exponential increase in computational cost with the register length, This paper considers iterative estimation algorithms based on the expectation-maximization (EM) algorithm and related space alternating methods. This approach is inspired in the parallelism that may be drawn between chaotic estimation and multiuser detection, which also becomes prohibitively complex as the number of users increases. The resulting algorithms are based on an iterative updating of estimates of the chaotic signal itinerary. Computer simulations show that the proposed algorithms achieve the performance of the ML estimator for short data registers and improve the computationally feasible (suboptimal) estimators for long records.

关键词： chaos maximum likelihood estimation piecewise linear techniques signal detection iterative methods optimisation signal processing maximum-likelihood estimation chaotic signals piecewise-linear maps space alternating methods multiuser detection iterative updating performance ML estimator short data registers suboptimal estimators long records expectation-maximization algorithm EM algorithm iterative estimation

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3D image reconstruction algorithms for cryo electron microscopy images of virus particles

3D image reconstruction algorithms for cryo electron microsc...

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Conference on Image Reconstruction from Incomplete Data

作者： Doerschuk, PC Johnson, JE Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA

A statistical model for the object and the complete image formation process in cryo electron microscopy of viruses is presented. Using this model, maximum likelihood reconstructions of the 3D structure of viruses are ... 详细信息

ISBN: (纸本)0819437689

关键词： cryo electron microscopy expectation-maximization algorithm statistical inverse problems viruses

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EM-based Channel Estimation in OFDM Systems with Phase Noise

EM-based Channel Estimation in OFDM Systems with Phase Noise

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54th Annual IEEE Global Telecommunications Conference (GLOBECOM)

作者： Carvajal, Rodrigo Agueero, Juan C. Godoy, Boris I. Goodwin, Graham C. Univ Newcastle Callaghan NSW 2308 Australia

ISBN: (纸本)9781424492688

In this paper we address the joint estimation of phase noise (PHN) and channel impulse response (CIR) in orthogonal frequency division multiplexing (OFDM) systems. We solve the estimation problem utilizing an algorithm based on a Monte Carlo (MC) implementation of the expectation-maximization (EM) algorithm. Our approach exploits the linear and Gaussian structure associated with the transmitted signal. We also focus on the impact that inaccurate estimation of PHN bandwidth has on the accuracy of the channel estimates. We show the benefits of the proposed algorithm via simulation studies.

关键词： Approximation methods Bandwidth Channel estimation EM-based channel estimation Estimation Gaussian processes Gaussian structure Monte Carlo implementation Monte Carlo methods Monte Carlo methods OFDM OFDM modulation OFDM systems Phase noise channel impulse response expectation-maximisation algorithm expectation-maximization algorithm linear structure orthogonal frequency division multiplexing phase noise phase noise

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Robust Hypersphere Fitting from Noisy Data Using an EM algorithm 29

Robust Hypersphere Fitting from Noisy Data Using an EM Algor...

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29th European Signal Processing Conference (EUSIPCO)

作者： Lesouple, Julien Pilastre, Barbara Altmann, Yoann Tourneret, Jean-Yves TeSA Toulouse France Heriot Watt Univ Edinburgh Midlothian Scotland Univ Toulouse INP ENSEEIHT IRIT TeSA Toulouse France

ISBN: (纸本)9789082797060

This article studies a robust expectation maximization (EM) algorithm to solve the problem of hypersphere fitting. This algorithm relies on the introduction of random latent vectors having independent von Mises-Fisher distributions defined on the hypersphere and random latent vectors indicating the presence of potential outliers. This model leads to an inference problem that can be solved with a simple EM algorithm. The performance of the resulting robust hypersphere fitting algorithm is evaluated for circle and sphere fitting with promising results in terms of both estimation performance and computation time.

关键词： Robust estimation hypersphere fitting expectation-maximization algorithm

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