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检索条件"主题词=Expectation maximization algorithm"

共 647 条记录，以下是301-310 订阅

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Unsupervised Satellite Image Segmentation using a Bivariate Beta type-II Mixture Model

Unsupervised Satellite Image Segmentation using a Bivariate ...

引用

IEEE 9th International Conference on Computational Cybernetics (ICCC)

作者： Ben Arab, Taher Zribi, Mourad Masmoudi, Afif Lab Informat Signal & Image Cote Opale LISIC EA 4 ULCO50 Rue Ferdinand Buisson BP 719 F-62228 Calais France Univ Sfax Fac Sci Lab Probabilities Statist Sfax Tunisia

ISBN: (纸本)9781479900633;9781479900602

This paper presents an unsupervised satellite color image segmentation approach based on Bivariate Beta type-II. Such a method could be considered as original since it uses a. K-Means clustering algorithm in order to initialize the image class number. Moreover, it exploits a Bivariate Beta type-II for statistical distributions applying it for each class. Satellite image exploitation requires the use of different approaches, especially those based on the unsupervised statistical segmentation principle. Such approaches necessitate the definition of several parameters such as image class number, class variables estimation and mixture distributions. The use of statistical image attributes has allowed us to get convincing results, provided that we ensure under the condition of having an initialization step with appropriate statistical distributions. Bivariate Beta type-II associated with a. K-means clustering algorithm and expectation-maximization (EM) algorithm could be adapted to such a problem. For each image class, Bivariate Beta typeII attributes a specific distribution type according to different parameters. Different adapted algorithms (namely. K-Means clustering algorithm, EM algorithm and Bivariate Beta type-II algorithm) are then applied to the satellite image segmentation problem. The efficiency of those combined algorithms is validated with the Mean Squared Errors (MSE), Signal to Noise Ratio (SNR) and Maximum Distance (MD).

关键词： Bivariate Beta type-II expectation maximization algorithm K-means clustering algorithm Mean Squared Errors Unsupervised color image segmentation

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Misspecified Cramer-Rao Bounds for Anomalous Clock Data in Satellite Constellations 32

Misspecified Cramer-Rao Bounds for Anomalous Clock Data in S...

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32nd European Signal Processing Conference (EUSIPCO)

作者： McPhee, Hamish Tourneret, Jean-Yves Valat, David Delporte, Jerome Gregoire, Yoan Paimblanc, Philippe Univ Toulouse Res Lab TeSA Toulouse France Univ Toulouse Res Lab IRIT Toulouse France French Space Agcy CNES Timing & Frequency Toulouse France French Space Agcy CNES Nav & Signal Proc Toulouse France Res Lab TeSA Toulouse France

ISBN: (纸本)9789464593617;9798331519773

Robust estimation methods are useful in mitigating the impact of anomalies in clock data. Such anomalous clock data is assumed to be well modeled by a Student's t-distribution. This paper derives a lower bound on the performance of the misspecified Gaussian model using the theory of the Misspecified Cramer-Rao bound (MCRB). The results of these derivations are verified by analyzing the Mean Square Error (MSE) of the misspecified Gaussian Maximum Likelihood Estimator (MLE) when using data generated by the Student's t-distribution. The derived MCRB indicates a constraint on the MSE when assuming a Gaussian distribution. The MLE for the mean of the Student's t-distribution is obtained with an expectation maximization algorithm and is shown to obtain a lower MSE than the MCRB and hence, the misspecified estimator. This indicates an improvement in performance if anomalous clock data is appropriately accounted for in the statistical model.

关键词： expectation maximization algorithm

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Land use/land cover change detection based on canonical transformation

Land use/land cover change detection based on canonical tran...

引用

Conference on Image Processing and Pattern Recognition in Remote Sensing

作者： Zhang, L Liao, MS Sheng, H Wuhan Univ Natl Lab Informat Engn Surveying Mapping & Remote Wuhan 430079 Hubei Peoples R China

ISBN: (纸本)081944684X

Change detection is a key topic in land use/land cover related studies and significant efforts have been made in the development of methods for change detection. In this article a multivariate analysis method based on canonical transformation is introduced into change detection using multi-temporal remote sensing imageries. Afterwards an automatic unsupervised discriminating technique based on the Bayes-Rule of Minimum Error is employed for changed areas identification in the difference image. Experimental results of a case study using Landsat TM imageries are presented to demonstrate the effectiveness of our method.

关键词： land use/land cover change detection canonical transformation multivariate alteration detection Bayes Rule of Minimum Error expectation maximization algorithm

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Label Fusion Based Brain MR Image Segmentation via a Latent Selective Model 9

Label Fusion Based Brain MR Image Segmentation via a Latent ...

引用

9th International Conference on Graphic and Image Processing (ICGIP)

作者： Liu, Gang Guo, Xiantang Zhu, Kai Liao, Hengxu Shanghai Univ Elect Power Sch Automat Engn Shanghai 200090 Peoples R China State Grid Corp China Jinhua Power Supply Co Hangzhou 321000 Zhejiang Peoples R China

ISBN: (数字)9781510617421

ISBN: (纸本)9781510617421

Multi-atlas segmentation is an effective approach and increasingly popular for automatically labeling objects of interest in medical images. Recently, segmentation methods based on generative models and patch-based techniques have become the two principal branches of label fusion. However, these generative models and patch-based techniques are only loosely related, and the requirement for higher accuracy, faster segmentation, and robustness is always a great challenge. In this paper, we propose novel algorithm that combines the two branches using global weighted fusion strategy based on a patch latent selective model to perform segmentation of specific anatomical structures for human brain magnetic resonance (MR) images. In establishing this probabilistic model of label fusion between the target patch and patch dictionary, we explored the Kronecker delta function in the label prior, which is more suitable than other models, and designed a latent selective model as a membership prior to determine from which training patch the intensity and label of the target patch are generated at each spatial location. Because the image background is an equally important factor for segmentation, it is analyzed in label fusion procedure and we regard it as an isolated label to keep the same privilege between the background and the regions of interest. During label fusion with the global weighted fusion scheme, we use Bayesian inference and expectation maximization algorithm to estimate the labels of the target scan to produce the segmentation map. Experimental results indicate that the proposed algorithm is more accurate and robust than the other segmentation methods.

关键词： Multi-atlas segmentation latent selective model patch-based segmentation global weighted fusion image background analysis bayesian inference expectation maximization algorithm

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Optimizing Vehicle Positioning via Multi-model Image and Radio Frequency Fusion 59

Optimizing Vehicle Positioning via Multi-model Image and Rad...

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59th Annual IEEE International Conference on Communications (IEEE ICC)

作者： Huan, Ouwen Chen, Mingzhe Luo, Tao Beijing Univ Posts & Telecommun Beijing Lab Adv Informat Network Beijing 100876 Peoples R China Univ Miami Dept Elect & Comp Engn Coral Gables FL 33146 USA Univ Miami Frost Inst Data Sci & Comp Coral Gables FL 33146 USA

ISBN: (纸本)9781728190549

In this paper, a multi-modal vehicle positioning framework that jointly localizes vehicles with channel state information (CSI) and images is designed. In particular, we consider an outdoor scenario where each vehicle can communicate with only one base station (BS), and hence, it can upload its estimated CSI to only its associated BS. Each BS is equipped with a set of cameras, such that it can collect a small number of labeled CSI, a large number of unlabeled CSI, and the images taken by cameras. To exploit the unlabeled CSI data and position labels obtained from images, we design a hard expectation-maximization (EM) based deep learning (DL) algorithm. Specifically, since we do not know the corresponding relationship between unlabeled CSI and the multiple vehicle locations in images, we formulate the calculation of the log-likelihood function as a maximum matching problem. Subsequently, the model parameters are updated according to the maximum matching between unlabeled CSI and position labels obtained from images. Simulation results show that the proposed method can reduce the positioning error by up to 60% compared to a baseline that does not use images and uses only CSI fingerprint for vehicle positioning.

关键词： expectation maximization algorithm

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Load Pseudomeasurements in Distribution System State Estimation

Load Pseudomeasurements in Distribution System State Estimat...

引用

PowerTech, 2015 IEEE Eindhoven

作者： Pieri, Georgia Asprou, Markos Kyriakides, Elias Univ Cyprus Dept Elect & Comp Engn KIOS Ctr Intelligent Syst & Networks Nicosia Cyprus

ISBN: (纸本)9781479976959

Distribution system state estimation (DSSE) is an essential tool in active distribution grids in order to enhance the awareness of the power system operators regarding the state of the distribution system. One of the peculiarities of the DSSE is the lack of adequate real-time measurements. Thus, in this paper a three-phase DSSE algorithm enhanced with an advanced approach for modeling pseudomeasurements is proposed. The major innovation of the proposed methodology is the development of a DSSE algorithm based on limited real-time measurements. The proposed DSSE algorithm takes into account the unbalanced nature of the distribution systems and the presence of virtual measurements. The pseudomeasurements are obtained from historical data, through a Gaussian Mixture Model (GMM) methodology, improved by the Calinski & Harabasz (CH) criterion and the expectation maximization (EM) algorithm. The performance of the proposed methodology is illustrated on the IEEE 13 node test feeder and compared with the case where the pseudomeasurements are modeled through a normal distribution.

关键词： Calinski & Harabasz criterion Distribution System State Estimation expectation maximization algorithm Gaussian Mixture Model pseudomeasurements

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Multiple Model LPV Approach to Identification of Nonlinear Dual-rate System with Random Time Delay

Multiple Model LPV Approach to Identification of Nonlinear D...

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17th IFAC Symposium on System Identification (SYSID 2015)

作者： Chen, Lei Huang, Biao Ding, Yongsheng Donghua Univ Coll Informat Sci & Technol Minist Educ Engn Res Ctr Digitized Text & Fash Technol Shanghai 201620 Peoples R China Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 2G6 Canada

This paper deals with the problem of nonlinear dual-rate system identification with random time delay. The proposed approach adopts the multiple modeling framework, and the global LPV model is represented by a combination of various local models weighted by a probability function. The considered structure of the local model is in a state space from, and the process has fast rate inputs and slow rate outputs with random time delay. The expectation maximization algorithm is utilized to formulate and solve the problem of interest. The parameters of the local models and the weighting functions are estimated simultaneously. The particle smoothing technique is adopted to handle the computation of expectation functions. The effectiveness of the proposed approach is further illustrated through a simulation example. (C) 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Multiple models expectation maximization algorithm Nonlinear process System identification Time delay

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Adaptive Soft Sensor Modeling Based on Weighted Supervised Latent Factor Analysis with Selectively Integrated Moving Windows

Adaptive Soft Sensor Modeling Based on Weighted Supervised L...

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20th World Congress of the International-Federation-of-Automatic-Control (IFAC)

作者： Yao, Le Ge, Zhiqiang Yuan, Xiaofeng Wang, Peiliang Zhejiang Univ Coll Control Sci & Engn Inst Ind Proc Control State Key Lab Ind Control Technol Hangzhou 310027 Zhejiang Peoples R China Cent S Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China Huzhou Univ Sch Engn Huzhou 313000 Peoples R China

An adaptive soft sensor modeling method based on weighted supervised latent factor analysis is proposed. In conventional moving window based adaptive soft sensor, predictive model is constructed only with the latest process information. To fully take advantage of the past windows, a set of recent local models are integrated by the Hayes' rule for quality estimation However, the former built models may contain similar information about the process, and the redundancy would increase the calculation with a low-efficient accuracy improvement. Then a selecting method is proposed through a statistical hypothesis testing to determine whether a window dataset should be retained or not. In this way, the mostly informative models are left to integrate an efficient predictive model. A real industrial case demonstrates the feasibility and efficiency of the proposed adaptive soft sensor. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Supervised latent factor analysis Weighted model expectation maximization algorithm Adaptive soft sensor Moving window Model integration Quality prediction

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Image reconstruction in electron microscopy

Image reconstruction in electron microscopy

引用

Conference on Image Reconstruction from Incomplete Data V

作者： Zheng, Yili Lee, Seunghee Doerschuk, Peter C. Purdue Univ W Lafayette IN 47907 USA

ISBN: (纸本)9780819472960

Maximum likelihood statistical algorithms are described for estimating the 3-D variation of the electron scattering intensity of biological objects from cryo electron microscopy images of multiple instances of the object. Three virus objects, two spherical and one helical, are considered. Solution of the maximum likelihood problem by expectation maximization algorithms or by direct maximization of the log likelihood requires large scale computing and end-to-end codesign of biological problem formulation, statistical models, algorithms, and software design and implementation have contributed to achieving practical results.

关键词： Cryo electron microscopy cryo EM heterogeneous spherical virus homogeneous spherical virus helical virus maximum likelihood statistical reconstruction 3-D reconstruction tomography nonlinear reconstruction expectation maximization algorithm

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Channel estimation and data detection with Fuzzy C-Means based EM approach in MIMO system 2

Channel estimation and data detection with Fuzzy C-Means bas...

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2nd International Conference on Computational Intelligence & Communication Technology (CICT)

作者： Dasani, Kirti Shrotriya, Anshul Medi Caps Inst Technol & Management Elect & Commun Indore Madhya Pradesh India

ISBN: (纸本)9781509002108

Classical wireless communication technologies are threatened with so many challenges for meeting the desires of ubiquity and mobility for the cellular systems. Hostile wireless channels features and restricted frequency bandwidths are obstacles in future generation systems. In order to deal with these limitations, different advanced signal processing approaches, such as expectation-maximization (EM) algorithm, SAGE algorithm, Baum-Welch algorithm, Kalman filters and their extensions etc. were proposed. In this paper, estimation of unknown channel parameter and detection of data at receiver end has been performed. MIMO Rayleigh and Rician channels are taken for wireless communication. To find the initial point for EM algorithm FCM clustering algorithm is used. In this work, algorithm is implemented using MATLAB R2012a. The performance matrices of the algorithm are bit error rate (BER) and mean square error (MSE) at different values of signal to noise ratio.

关键词： MIMO system Fuzzy C-Mean expectation maximization algorithm semi blind channel estimation channel state information maximum a posterior

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