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检索条件"主题词=Expectation-maximization algorithms"

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Multi-frame image fusion using the expectation-maximization algorithm

Multi-frame image fusion using the expectation-maximization ...

引用

International Conference on Information Fusion

作者： J. Yang R.S. Blum ECE Department Lehigh University Bethlehem PA USA

A multi-frame image fusion scheme is proposed to fuse visual and thermal images for night vision applications. While many previous image fusion approaches perform the fusion on a frame-by-frame basis, this method considers optimum use of neighboring frames to incorporate temporal as well as sensor fusion. This fusion scheme is based on a statistical image formation model. The multiple sensor image frames are described as the true scene corrupted by additive non-Gaussian distortion. The expectation-maximization (EM) algorithm is used to estimate the parameters in the model and to produce the final fused result. The experimental results showed that the EM-based multi-frame image fusion scheme has significant advantage in terms of sensor noise reduction.

关键词： Image fusion expectation-maximization algorithms Sensor fusion Image sensors Fuses Night vision Sensor phenomena and characterization Layout Parameter estimation Noise reduction

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Turbo synchronization: a combined sum-product and expectation-maximization algorithm approach

Turbo synchronization: a combined sum-product and expectatio...

引用

IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： C. Herzet V. Ramon L. Vandendorpe Communications Laboratory Université catholique de Louvain Louvain-la-Neuve Belgium

This paper places turbo synchronization into the sum-product (SP) and the expectation-maximization (EM) algorithm framework. In particular, we show that the combination of these algorithms enables to design low-complexity and very powerful synchronisers. The proposed synchronizer is compared to a previously-proposed EM framework. As the derivation suggests, our new iterative scheme clearly outperforms the classical use of the EM algorithm.

关键词： expectation-maximization algorithms Iterative algorithms Iterative decoding Maximum likelihood decoding Frequency synchronization AWGN Timing Baseband Laboratories Concatenated codes

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Novel EM-based Method for Channel Estimation and Signal Detection in MIMO-OFDM Systems

Novel EM-based Method for Channel Estimation and Signal Dete...

引用

Eurocon

作者： D. Obradovic A. Szabo D. Mandic Rui Chang Technology division Siemens Corporate Munich Germany Electrical Engineering Department Imperial College London London UK

This paper presents a novel method for channel estimation and signal detection in MIMO-OFDM systems based on the expectation-maximization algorithm (EM). In contrast to standard pilot aided interpolation methods, the estimation of the channel in a given frequency bin without pilot symbols is posed as a maximum likelihood (ML) with missing observations and is solved by the iterative EM algorithm. As a semi-blind estimation method, EM is insensitive to permutation and scaling of transmitted signals. To avoid this uncertainty, the initialization of the iterative optimization for each frequency bin is achieved using the model of the neighboring bin, provided that these bins satisfy thus introduced similarity condition. Hence, channel estimation is performed sequentially over all bins starting from the bin for which its model is estimated with a pilot sequence. The proposed method is tested successfully on MIMO-OFDM systems in realistic channel conditions

关键词： Channel estimation Signal detection Frequency estimation Maximum likelihood estimation expectation-maximization algorithms Interpolation Maximum likelihood detection Iterative methods Iterative algorithms Uncertainty

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The EM algorithm in independent component analysis

The EM algorithm in independent component analysis

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： K.B. Petersen O. Winther ISP IMM Technical University of Denmark Kongens Lyngby Denmark

We investigate two techniques for independent component analysis which use the expectation-maximization algorithm. Analysis and simulations show that convergence becomes extraordinarily slow for almost all cases, compared to other optimization techniques. The two alternatives considered are "adaptive overrelaxed EM" and Ucminf (a BFGS with soft line search), which both improves the convergence dramatically with little or no extra analytical work. We discuss the generality and perspectives of the findings.

关键词： Independent component analysis Convergence Covariance matrix Acceleration Algorithm design and analysis Cost function expectation-maximization algorithms Analytical models Maximum likelihood estimation Proposals

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Affine object tracking with kernel-based spatial-color representation

Affine object tracking with kernel-based spatial-color repre...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Haihong Zhang Weimin Huang Zhiyong Huang Liyuan Li Institute for Infocomm Research Singapore School of Computing National University of Singapore Singapore

ISBN: (纸本)0769523722

This paper presents a new visual tracking method that can achieve accurate estimation of affine transformation and precise spatial-color representation. The estimation of transformation provides more information than translation for better motion understanding and also helps maintain the precise representation; the precise representation enables tracking objects in highly-cluttered environment. The basis of the method is a kernel-based similarity measure called affine matching that describes the relationship between image regions with respect to affine transformation parameters. Based on the similarity measure, a mathematical solution is derived for estimating the transformation parameters for moving objects in videos. Various experiments have yielded positive results.

关键词： Kernel Computer vision Target tracking Histograms Deformable models Motion estimation Parameter estimation Videos Gaussian distribution expectation-maximization algorithms

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Hidden Markov Modeling of Noise Periodograms Using Rayleigh Mixture Models

Hidden Markov Modeling of Noise Periodograms Using Rayleigh ...

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Asilomar Conference on Signals, Systems & Computers

作者： K.V. Sorensen S.V. Andersen Department of Communication Technology University of Aalborg Aalborg Denmark

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LADAR Range Image Segmentation using Curve Evolution and Maximum Likelihood Estimation

LADAR Range Image Segmentation using Curve Evolution and Max...

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Asilomar Conference on Signals, Systems & Computers

作者： Haihua Feng W.C. Karl D.A. Castanon MathWorks Inc. Natick MA USA ECE Department Boston University Boston MA USA

In this paper, we develop a new maximum likelihood-based, curve evolution approach for laser radar range image segmentation. This approach combines a hybrid scene model for representing the range distribution of the field and a statistical mixture model for the range data measurement noise. The image segmentation problem is formulated as an energy minimization problem which jointly estimates the target boundary together with the target region intensity and background texture directly from the noisy range data. Curve evolution techniques and an expectation-maximization algorithm are jointly employed as an efficient solver for minimizing the objective energy

关键词： Laser radar Image segmentation Maximum likelihood estimation Laser modes Laser noise Radar imaging Layout Noise measurement Background noise expectation-maximization algorithms

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Detecting denial-of-service attacks with incomplete audit data

Detecting denial-of-service attacks with incomplete audit da...

引用

International Conference on Computer Communications and Networks (ICCCN)

作者： A. Patcha J.-M. Park Bradley Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University Blacksburg VA USA

With the ever increasing deployment and usage of gigabit networks, traditional network anomaly detection based intrusion detection systems have not scaled accordingly. Most, if not all, systems deployed assume the availability of complete and clean data for the purpose of intrusion detection. We contend that this assumption is not valid. Factors like noise in the audit data, mobility of the nodes and the large amount of network data generated by the network make it difficult to build a normal traffic profile of the network for the purpose of anomaly detection. From this perspective, we present an anomaly detection scheme, called SCAN (stochastic clustering algorithm for network anomaly detection), that has the capability to detect intrusions with high accuracy even when audit data is not complete. We use the expectation-maximization algorithm to cluster the incoming audit data and compute the missing values in the audit data. We improve the speed of convergence of the clustering process by using Bloom filters and data summaries. We evaluate SCAN using the 1999 DARPA/Lincoln Laboratory intrusion detection evaluation dataset.

关键词： Computer crime Intrusion detection Noise generators Telecommunication traffic Stochastic resonance Clustering algorithms expectation-maximization algorithms Convergence Filters Laboratories

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Multiscale object features from clustered complex wavelet coefficients

Multiscale object features from clustered complex wavelet co...

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IEEE/SP Workshop on Statistical Signal Processing (SSP)

作者： R. Anderson N. Kingsbury J. Fauqueur Signal Processing Group Department of Engineering University of Cambridge UK

This paper introduces a method by which intuitive feature entities can be created from ILP (InterLevel Product) coefficients. The ILP transform is a pyramid of decimated complex-valued coefficients at multiple scales, derived from dual-tree complex wavelets, whose phases indicate the presence of different feature types (edges and ridges). We use an expectation-maximization algorithm to cluster large ILP coefficients that are spatially adjacent and similar in phase. We then demonstrate the relationship that these clusters possess with respect to observable image content, and conclude with a look at potential applications of these clusters, such as rotation- and scale-invariant object recognition.

关键词： Wavelet coefficients Shape Object recognition Signal processing Wavelet transforms expectation-maximization algorithms Image retrieval Acceleration Signal processing algorithms Continuous wavelet transforms

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Cooperative Decoding and Channel Estimation for Superimposed Space-Time Interleaved Coded Signals

Cooperative Decoding and Channel Estimation for Superimposed...

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Berthet Visoz Pourmir SUPELEC Department of Telecommunications France FranceTélécom France Department of Telecommunications SUPELEC Gif-sur-Yvette France

This paper addresses the problem of transmitting space-time interleaved coded signals over multiple-input multiple-output (MIMO) block fading channels with intersymbol interference (ISI). At the receiver, we investigate a cooperative approach in which data detection, decoding, and channel estimation are performed separately and iteratively. Computationally efficient methods based on the expectation-maximization (EM) algorithm are developed for multiple path gain estimation

关键词： Channel estimation MIMO Fading Intersymbol interference Iterative decoding AWGN Telecommunications expectation-maximization algorithms Interleaved codes Degradation

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