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Soft shape registration under Lie group frame

IET COMPUTER VISION 2013年第6期7卷 437-447页

作者： Peng, Yaxin Lin, Wei Ying, Shihui Peng, Jigen Shanghai Univ Sch Sci Dept Math Shanghai 200444 Peoples R China Xi An Jiao Tong Univ Sch Math & Stat Inst Informat & Syst Sci Xian 710049 Peoples R China

In this study, the authors address a two-dimensional (2D) shape registration problem on data with anisotropic-scale deformation and noise. First, the model is formulated under the iterative closest point (ICP) framework, which is one of the most popular methods for shape registration. To overcome the effect of noise, the expectation maximisation algorithm is used to improve the model. Then, the structure of Lie groups is adopted to parameterise the proposed model, which provides a unified framework to deal with the shape registration problems. Such representation makes it possible to introduce some suitable constraints to the model, which improves the robustness of the algorithm. Thereby, the 2D shape registration problem is turned to an optimisation problem on the matrix Lie group. Furthermore, a sequence of quadratic programming is designed to approximate the solution for the model. Finally, several comparative experiments are carried out to validate that the authors' algorithm performs well in terms of robustness, especially in the presence of outliers.

关键词： expectation-maximisation algorithm image registration Lie groups quadratic programming quadratic programming matrix Lie group optimisation problem expectation maximisation algorithm ICP framework iterative closest point noise anisotropic-scale deformation 2D shape registration problem Lie group frame soft shape registration

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Joint estimation and identification for stochastic systems with unknown inputs

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IET CONTROL THEORY AND APPLICATIONS 2013年第10期7卷 1377-1386页

作者： Lan, Hua Liang, Yan Yang, Feng Wang, Zengfu Pan, Quan Northwestern Polytech Univ Sch Automat Xian 710072 Shaanxi Peoples R China

Motivated by tracking a manoeuvring target in electronic counter environments, the authors present the problem of joint estimation and identification of a class of discrete-time stochastic systems with unknown inputs in both the plant and sensors. Based on the expectation-maximum criterion, the joint optimisation scheme of state estimation, parameter identification and iteration terminate decision were derived. A numerical example of tracking a manoeuvring target accompanied range gate pull-off is utilised to verify the proposed scheme.

关键词： discrete time systems expectation-maximisation algorithm optimisation parameter estimation state estimation stochastic systems target tracking stochastic system identification manoeuvring target tracking electronic counter environments stochastic system estimation discrete-time stochastic systems unknown inputs expectation-maximum criterion joint optimisation scheme state estimation parameter identification iteration terminate decision

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Measurement of Stable Isotope Ratios in Methylamphetamine: A Link to Its Precursor Source

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ANALYTICAL CHEMISTRY 2013年第19期85卷 9400-9408页

作者： Salouros, Helen Sutton, Gordon J. Howes, Joanna Hibbert, D. Brynn Collins, Michael Natl Measurement Inst N Ryde NSW Australia Univ Technol Ultimo NSW Australia Univ New S Wales Sch Chem Sydney NSW 2052 Australia

The illicit drug methylamphetamine is often prepared from the precursor ephedrine or pseudoephedrine, which in turn are obtained by three processes: extraction from the Ephedra plant ("natural"), via fermentation of sugars ("semi-synthetic"), and by a "fully synthetic" route from propiophenone. We report the first method to differentiate between the three industrial routes used to produce the precursors ephedrine and pseudoephedrine by measurement of stable isotope ratios of nitrogen (delta N-15), hydrogen (delta H-2), and carbon (delta C-13). Analysis of 782 samples of seized methylamphetamine allowed classification into three groups using k-means clustering or the expectation-maximization algorithm applied to a Gaussian mixture model. By preparation of 30 samples of ephedrine by the "fully synthetic" industrial process and measuring their delta N-15, delta H-2, and delta C-13 values, we observed that N-15 becomes significantly depleted compared to the methylamine starting material. Conversion of ten ephedrine samples to methylamphetamine showed that this depletion is maintained in the final drug product, of which the delta N-15, delta C-13, and delta H-2 values were distinct from those of ephedrine and methylamphetamine samples of a semi-synthetic (fermentation pathway) origin. Combining modeling analysis with the new experiments and published information on the values of delta H-2 gave a definitive assignment of the three model groups, and equations to obtain probabilities for the precursor origin of any new sample. A simple rule of thumb is also presented. Making an assignment using delta values is particularly useful when no other chemical profiling information is available.

关键词： Heuristics Methamphetamine Pharmaceutical Preparations Ephedrine expectation-maximisation algorithm Pseudoephedrine stable isotopes isotope ratios Propiophenones

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Gaussian mixture model approximation of total spatial power spectral density for multiple incoherently distributed sources

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IET SIGNAL PROCESSING 2013年第4期7卷 306-311页

作者： Wang, Huigang Li, Shanlong Li, Huxiong Shi, Yang Northwestern Polytech Univ Coll Marine Xian 710072 Peoples R China Wenzhou Univ Sch Comp Engn Wenzhou Peoples R China Univ Victoria Dept Mech Engn Victoria BC V8N 3P6 Canada

Practically, the spatial power spectral density (PSD) of single or multiple incoherently distributed (ID) sources is often unknown, and the total spatial PSD is suitable to model the spatial distribution characteristic of signals if the number of multiple ID sources is also unknown. In this study, the Gaussian mixture model (GMM) is employed to characterise the total spatial PSD of multiple ID sources, and two algorithms are proposed to estimate the parameters of the GMM. The first one is the covariance fitting method for multiple ID sources with Gaussian PSD, and the other is the iterative expectation maximisation (EM) algorithm. Simulation studies demonstrate that the EM algorithm outperforms other methods in approximating the shape of the total spatial PSD, especially for small spatial spread.

关键词： expectation-maximisation algorithm IDAHO OPERATIONS OFFICE Gm(m)

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Image segmentation by a new weighted Student's t-mixture model

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IET IMAGE PROCESSING 2013年第3期7卷 240-251页

作者： Zhang, Hui Wu, Qing Ming Jonathan Thanh Minh Nguyen Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing Jiangsu Peoples R China Univ Windsor Dept Elect & Comp Engn Windsor ON N9B 3P4 Canada Nanjing Univ Informat Sci & Technol Jiangsu Engn Ctr Network Monitoring Nanjing Jiangsu Peoples R China

In this study, the authors introduce a new weighted Student's t-mixture model (WSMM) for image segmentation. Gaussian distribution and Student's t-distribution are the two commonly used probabilities in the finite mixture model (FMM). The Student's t-mixture model has come to be regarded as an alternative to Gaussian mixture models, as it is heavily tailed and more robust for outliers. Moreover, the pixels are considered independent of each other in the FMM. Although some existing methods incorporate the spatial relationship between neighbouring pixels, they do not consider the relationship between spatial information and clustering information, thus those reported methods remain sensitive to noise. The advantages of the authors method are as follows: first, the authors introduce WSMM to incorporate the local spatial information, pixel intensity value and clustering information in an image. Second, the authors model is simple, easy to implement and has a good balance between noise insensitiveness and image detail preservation. Third, they adopt the gradient method and expectation maximisation algorithm, which allow for simultaneous estimation of optimal parameters. Finally, the most useful statistical tool for image segmentation, the well-known hidden Markov random field model, is a special case of their model. Thus, their method is general enough for model-based techniques construction. Experimental results on synthetic and real images demonstrate the improved robustness and effectiveness of their approach.

关键词： expectation-maximisation algorithm Gaussian distribution gradient methods hidden Markov models image segmentation pattern clustering image segmentation weighted student t-mixture model WSMM Gaussian distribution student t-distribution finite mixture model FMM Gaussian mixture models neighbouring pixels clustering information local spatial information pixel intensity value noise insensitiveness image detail preservation gradient method expectation maximisation algorithm statistical tool hidden Markov random field model model-based techniques construction synthetic images real images expectation-maximisation algorithm Gaussian distribution gradient methods hidden Markov models image segmentation pattern clustering image segmentation weighted student t-mixture model WSMM Gaussian distribution student t-distribution finite mixture model FMM Gaussian mixture models neighbouring pixels clustering information local spatial information pixel intensity value noise insensitiveness image detail preservation gradient method expectation maximisation algorithm statistical tool hidden Markov random field model model-based techniques construction synthetic images real images

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Retinal vessel classification: sorting arteries and veins

Retinal vessel classification: sorting arteries and veins

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35th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Relan, D. MacGillivray, T. Ballerini, L. Trucco, E. Univ Edinburgh Clin Res Imaging Ctr Edinburgh EH8 9YL Midlothian Scotland

ISBN: (纸本)9781457702167

For the discovery of biomarkers in the retinal vasculature it is essential to classify vessels into arteries and veins. We automatically classify retinal vessels as arteries or veins based on colour features using a Gaussian Mixture Model, an expectation-Maximization (GMM-EM) unsupervised classifier, and a quadrant-pairwise approach. Classification is performed on illumination-corrected images. 406 vessels from 35 images were processed resulting in 92% correct classification (when unlabelled vessels are not taken into account) as compared to 87.6%, 90.08%, and 88.28% reported in [12] [14] and [15]. The classifier results were compared against two trained human graders to establish performance parameters to validate the success of classification method. The proposed system results in specificity of (0.8978, 0.9591) and precision (positive predicted value) of (0.9045, 0.9408) as compared to specificity of (0.8920, 0.7918) and precision of (0.8802, 0.8118) for (arteries, veins) respectively as reported in [13]. The classification accuracy was found to be 0.8719 and 0.8547 for veins and arteries, respectively.

关键词： Gaussian distribution biomedical optical imaging blood vessels expectation-maximisation algorithm eye feature extraction image classification medical image processing retinal recognition

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Tracking Deformable Objects with Point Clouds

Tracking Deformable Objects with Point Clouds

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Schulman, John Lee, Alex Ho, Jonathan Abbeel, Pieter Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94709 USA

ISBN: (纸本)9781467356411;9781467356435

We introduce an algorithm for tracking deformable objects from a sequence of point clouds. The proposed tracking algorithm is based on a probabilistic generative model that incorporates observations of the point cloud and the physical properties of the tracked object and its environment. We propose a modified expectation maximization algorithm to perform maximum a posteriori estimation to update the state estimate at each time step. Our modification makes it practical to perform the inference through calls to a physics simulation engine. This is significant because (i) it allows for the use of highly optimized physics simulation engines for the core computations of our tracking algorithm, and (ii) it makes it possible to naturally, and efficiently, account for physical constraints imposed by collisions, grasping actions, and material properties in the observation updates. Even in the presence of the relatively large occlusions that occur during manipulation tasks, our algorithm is able to robustly track a variety of types of deformable objects, including ones that are one-dimensional, such as ropes;two-dimensional, such as cloth;and three-dimensional, such as sponges. Our implementation can track these objects in real time.

关键词： expectation-maximisation algorithm object tracking

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Image Segmentation Using a Spatially Correlated Mixture Model with Gaussian Process Priors

Image Segmentation Using a Spatially Correlated Mixture Mode...

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2nd IAPR Asian Conference on Pattern Recognition (ACPR)

作者： Kurisu, Kosei Suematsu, Nobuo Iwata, Kazunori Hayashi, Akira Hiroshima City Univ Grad Sch Informat Sci Hiroshima Japan

ISBN: (纸本)9781479921904

Finite mixture modeling has been widely used for image segmentation. However, since it takes no account of the spatial correlation among pixels in its standard form, its segmentation accuracy can be heavily deteriorated by noise in images. To improve segmentation accuracy in noisy images, the spatially variant finite mixture model has been proposed, in which a Markov Random Filed (MRF) is used as the prior for the mixing proportions and its parameters are estimated using the expectation-Maximization (EM) algorithm based on the maximum a posteriori (MAP) criterion. In this paper, we propose a spatially correlated mixture model in which the mixing proportions are governed by a set of underlying functions whose common prior distribution is a Gaussian process. The spatial correlation can be expressed with a Gaussian process easily and flexibly. Given an image, the underlying functions are estimated by using a quasi EM algorithm and used to segment the image. The effectiveness of the proposed technique is demonstrated by an experiment with synthetic images.

关键词： Gaussian processes finite mixture models image segmentation image segmentation Mixture models Gaussian processes Mix proportioning Gauss images Prior distribution expectation-maximisation algorithm Noise

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Factor Model Estimation By Using the Alpha-EM algorithm

Factor Model Estimation By Using the Alpha-EM Algorithm

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IEEE Global Conference on Signal and Information Processing (GlobalSIP)

作者： Jia, Tengjie SUNY Stony Brook Quantitat Finance Appl Math & Stat Stony Brook NY 11794 USA

ISBN: (纸本)9781479902484

in this paper, we present the alpha-EM algorithm for factor model estimation for given sample covariance. The alpha-EM includes the traditional log-EM as its proper subset. Since we use log-EM for factor analysis, however, it is shown that alpha-EM can also be used on factor analysis and more important the convergence speed of the alpha-EM is much faster than log-EM. It also allows us to choose different alphas to achieve the fastest convergence speed and more accurate factor model estimation.

关键词： expectation-maximisation algorithm Factor analysis Proper subset algorithms sample covariance Converge fast speed

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A Dynamical System Model for Neural Tracking of Speech with EEG

A Dynamical System Model for Neural Tracking of Speech with ...

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6th International IEEE EMBS Conference on Neural Engineering (NER)

作者： Rajaram, Siddharth Lalor, Edmund C. Shinn-Cunningham, Barbara G. Boston Univ Ctr Computatinal Neurosci & Neural Technol Boston MA 02215 USA Trinity Coll Dublin Trin Ctr Bioengn &Trin Dublin Ireland

ISBN: (纸本)9781467319690

In this paper we present a linear dynamical system to model the ongoing neural response, measured by EEG, while a listener is selectively attending a speech stream that is presented in a mixture. In our state-space formulation, the latent state variables represent the activity of the underlying neural substrates and the ongoing neural dynamics are captured by a multivariate autoregressive model with exogenous inputs (MVARX model). The observation model projects these neural sources onto the EEG montage. System identification is performed using a novel regularized expectation maximization (EM) algorithm for linear dynamical systems. The model was able to correctly identify which of two simultaneously presented speech streams was attended in roughly 85% of one minute long test trials, averaged over all subjects.

关键词： autoregressive processes electroencephalography expectation-maximisation algorithm medical signal processing neurophysiology speech speech processing

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