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检索条件"主题词=Expectation maximization algorithms"

共 128 条记录，以下是41-50 订阅

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A Bootstrapped PMHT with Feature Measurements and a New Way to Derive Its Information Matrix 26

A Bootstrapped PMHT with Feature Measurements and a New Way ...

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Conference on Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI

作者： Lu, Qin Domrese, Katherine Willett, Peter Bar-Shalom, Yaakov Pattipati, Krishna Univ Connecticut Dept Elect & Comp Engn Storrs CT 06269 USA

ISBN: (纸本)9781510609013;9781510609020

The probabilistic multiple-hypothesis tracker (PMHT), a tracking algorithm of considerable theoretical elegance based on the expectation-maximization (EM) algorithm, will be considered for the problem of multiple target tracking (MTT) with multiple sensors in clutter. Aside from position observations, continuous measurements associated with the unique and constant feature of each target are incorporated to jointly estimate the states and feature of the targets for the sake of tracking and classification, leading to a bootstrapped implementation of the PMHT. In addition, we rederived the information matrix for the big state vector stacking states for all the targets at all the time steps during the observation time. Simulation results have been conducted for both closely spaced and well separated scenarios with and without feature measurements. The normalized estimation error squared (NEES) calculated using the information matrix for both scenarios with and without feature measurements are within the 95% probability region. In other words, the estimates are consistent with the corresponding covariances.

关键词： expectation maximization algorithms Detection and tracking algorithms Sensors Feature extraction Motion measurement Motion models Algorithm development Matrices algorithms Time metrology

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Prospects and challenges in the electromagnetic follow-up of LIGO-Virgo gravitational wave transients 5

Prospects and challenges in the electromagnetic follow-up of...

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Conference on Observatory Operations - Strategies, Processes, and Systems V

作者： Vitale, Salvatore MIT LIGO Lab Cambridge MA 02139 USA

ISBN: (纸本)9780819496171

The kilometer-scale ground based gravitational wave (GW) detectors, LIGO and Virgo, are being upgraded to their advanced configurations. We expect the two LIGO observatories to undertake a 3 month science run in 2015 with a limited sensitivity. Virgo should come online in 2016, and join LIGO for a 6 month science run. Through a sequence of science runs and commissioning periods, the final sensitivity should be reached by similar to 2019. LIGO and Virgo are expected to deliver the first direct detection of gravitational wave transients in the next few years. Most of the known sources of GWs targeted by LIGO and Virgo will likely be luminous in the electromagnetic (EM) spectrum as well. Compact binary coalescences are thought to be progenitors of short gamma-ray bursts, while long gamma-ray bursts are likely to be associated with core collapse supernova. A joint detection of gravitational and EM radiation may help confirm these associations, and expand our understanding of those astrophysical systems. Due to the transient nature, a search for the EM counterparts to GW events should be done with the shortest latency. In this paper we describe the EM follow-up program of Advanced LIGO and Virgo, from the search for GWs to the production of sky maps. Furthermore, we quantify the expected sky localization errors in the first two years of operation of the advanced detectors network.

关键词： LIGO Sensors Signal to noise ratio Electromagnetism Gamma radiation Observatories Interferometers expectation maximization algorithms Signal detection Stars

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STATISTICAL INTENSITY CORRECTION AND SEGMENTATION OF MRI DATA

STATISTICAL INTENSITY CORRECTION AND SEGMENTATION OF MRI DAT...

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3rd Conference on Visualization in Biomedical Computing 1994 (VBC 94)

作者： WELLS, WM GRIMSON, WEL KIKINIS, R JOLESZ, FA HARVARD UNIV SCH MEDBOSTONMA 02115

ISBN: (纸本)0819416983

Many applications of MRI are facilitated by segmenting the volume spanned by the imagery into the various tissue types that are present. Intensity-based classification of MR images has proven to be problematic, even when advanced techniques such as non- parametric multi-channel methods are used. A persistent difficulty has been accommodating the spatial intensity inhomogeneities that are due to the equipment. This paper describes a statistical method that uses knowledge of tissue properties and intensity inhomogeneities to correct for these intensity inhomogeneities. Use of the expectation-maximization algorithm leads to a method (EM segmentation) for simultaneously estimating tissue class and the correcting gain field. The algorithm iterates two components to convergence: tissue classification, and gain field estimation. The result is a powerful new technique for segmenting and correcting MR images. An implementation of the method is discussed, and results are reported for segmentation of white matter and gray matter in gradient-echo and spin-echo images. Examples are shown for axial, coronal and sagittal (surface coil) images. For a given type of acquisition, intensity variations across patients, scans, and equipment have been accommodated without manual intervention in the segmentation. In this sense, the method is fully automatic for segmenting healthy brain tissue. An accuracy assessment was made in which the method was compared to manual segmentation, and to a method based on supervised multi-variate classification, in segmenting white matter and gray matter. The method was found to be consistent with manual segmentation, and closer to manual segmentation than the supervised method.

关键词： Image segmentation Tissues expectation maximization algorithms Magnetic resonance imaging Brain Linear filtering Neuroimaging Data modeling Image classification Statistical analysis

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A new clustering algorithm for scanning electron microscope images 27

A new clustering algorithm for scanning electron microscope ...

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Conference on Optical Pattern Recognition XXVII

作者： Yousef, Amr Duraisamy, Prakash Karim, Mohammad Univ Business & Technol Elect & Commun Dept Dahban Saudi Arabia Univ Alexandria Dept Mat Engn Alexandria Egypt Miami Univ Dept Comp Sci & Software Engn Oxford OH 45056 USA Univ Massachusetts Dartmouth Dept Elect Engn Dartmouth MA USA

ISBN: (纸本)9781510600867

A scanning electron microscope (SEM) is a type of electron microscope that produces images of a sample by scanning it with a focused beam of electrons. The electrons interact with the sample atoms, producing various signals that are collected by detectors. The gathered signals contain information about the sample's surface topography and composition. The electron beam is generally scanned in a raster scan pattern, and the beam's position is combined with the detected signal to produce an image. The most common configuration for an SEM produces a single value per pixel, with the results usually rendered as grayscale images. The captured images may be produced with insufficient brightness, anomalous contrast, jagged edges, and poor quality due to low signal-to-noise ratio, grained topography and poor surface details. The segmentation of the SEM images is a tackling problems in the presence of the previously mentioned distortions. In this paper, we are stressing on the clustering of these type of images. In that sense, we evaluate the performance of the well-known unsupervised clustering and classification techniques such as connectivity based clustering (hierarchical clustering), centroid-based clustering, distribution-based clustering and density-based clustering. Furthermore, we propose a new spatial fuzzy clustering technique that works efficiently on this type of images and compare its results against these regular techniques in terms of clustering validation metrics.

关键词： Scanning electron microscopy Image segmentation Electron microscopes Sensors Data modeling expectation maximization algorithms

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IMAGE-RESTORATION AND IDENTIFICATION USING THE EM AND ADAPTIVE RAP algorithms

IMAGE-RESTORATION AND IDENTIFICATION USING THE EM AND ADAPTI...

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CONF ON VISUAL COMMUNICATIONS AND IMAGE PROCESSING : IMAGE PROCESSING

作者： KUO, S MAMMONE, RJ Rutgers Univ. (United States)

ISBN: (纸本)0819407437

A new iterative algorithm for image restoration and point spread function (PSF) estimation is presented. The method initially estimates the PSF and the original image using the expectation maximization (EM) method. The resulting image estimate is then refined by using the adaptive Row Action Projection (RAP) algorithms which is based on the theory of Projection Onto Convex Sets (POCS). The new implementation of the RAP algorithm can be performed efficiently in parallel and facilitates locally adaptive constraints and cycling strategies. The PSF is re-estimated using a least square technique. Computer simulations illustrate the new method to be very competitive in restoring degraded images and estimating the PSF from noisy blurred images with unknown PSF.

关键词： Point spread functions expectation maximization algorithms Image processing Image restoration Autoregressive models Image analysis Visual communications algorithms Imaging systems Visual process modeling

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Sources of Uncertainty in Feature-Based Image Registration algorithms 6

Sources of Uncertainty in Feature-Based Image Registration A...

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Conference on Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VI

作者： Sundlie, Paul O. Taylor, Clark N. Fernando, Joseph A. Univ Dayton Res Inst Dayton OH 45469 USA US Air Force Res Lab Wright Patterson AFB OH USA

ISBN: (纸本)9781628415803

One significant technological barrier to enabling multi-sensor integrated ISR is obtaining an accurate understanding of the uncertainty present from each sensor. Once the uncertainty is known, data fusion, cross-cueing, and other exploitation algorithms can be performed. However, these algorithms depend on the availability of accurate uncertainty information from each sensor. In many traditional systems (e.g., a GPS/IMU-based navigation system), the uncertainty values for any estimate can be derived by carefully observing or characterizing the uncertainty of its inputs and then propagating that uncertainty through the estimation system. In this paper, we demonstrate that image registration uncertainty, on the other hand, cannot be characterized in this fashion. Much of the uncertainty in the output of a registration algorithm is due to not only the sensors used to collect the data, but also data collected and the algorithms used. In this paper, we present results of an analysis of feature-based image registration uncertainty. We make use of Monte Carlo analysis to investigate the errors present in an image registration algorithm. We demonstrate that the classical methods of propagating uncertainty from the inputs to the outputs yields significant underestimates of the true uncertainty on the output. We then describe at least two possible sources of additional error present in feature-based methods and demonstrate the importance of these sources of error.

关键词： Image registration Monte Carlo methods Sensors Navigation systems Statistical modeling Statistical analysis expectation maximization algorithms Evolutionary algorithms Image analysis Intelligence systems

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IMAGE-RECONSTRUCTION FOR 3-D LIGHT-MICROSCOPY WITH A REGULARIZED LINEAR METHOD INCORPORATING A SMOOTHNESS PRIOR

IMAGE-RECONSTRUCTION FOR 3-D LIGHT-MICROSCOPY WITH A REGULAR...

引用

CONF ON BIOMEDICAL IMAGE PROCESSING AND BIOMEDICAL VISUALIZATION

作者： PREZA, C MILLER, MI CONCHELLO, JA Washington Univ. (United States)

ISBN: (纸本)0819411388

We have shown that the linear least-squares (LLS) estimate of the intensities of a 3-D object obtained from a set of optical sections is unstable due to the inversion of small and zero-valued eigenvalues of the point-spread function (PSF) operator. The LLS solution was regularized by constraining it to lie in a subspace spanned by the eigenvectors corresponding to a selected number of the largest eigenvalues. In this paper we extend the regularized LLS solution to a maximum a posteriori (MAP) solution induced by a prior formed from a 'Good's like' smoothness penalty. This approach also yields a regularized linear estimator which reduces noise as well as edge artifacts in the reconstruction. The advantage of the linear MAP (LMAP) estimate over the current regularized LLS (RLLS) is its ability to regularize the inverse problem by smoothly penalizing components in the image associated with small eigenvalues. Computer simulations were performed using a theoretical PSF and a simple phantom to compare the two regularization techniques. It is shown that the reconstructions using the smoothness prior, give superior variance and bias results compared to the RLLS reconstructions. Encouraging reconstructions obtained with the LMAP method from real microscopical images of a 10 micrometers fluorescent bead, and a four-cell Volvox embryo are shown.

关键词： Point spread functions 3D image processing Microscopy Inverse optics expectation maximization algorithms Signal to noise ratio Computer simulations Fourier transforms Image processing Image restoration

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A Probabilistic Approach for the Registration of Images with Missing Correspondences

A Probabilistic Approach for the Registration of Images with...

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Conference on Medical Imaging: Image Processing

作者： Krueger, Julia Ehrhardt, Jan Schultz, Sandra Handels, Heinz Univ Lubeck Inst Med Informat Lubeck Germany

ISBN: (纸本)9781510625464

The registration of two medical images is usually based on the assumption that corresponding regions exist in both images. If this assumption is violated by e. g. pathologies, most approaches encounter problems. The here proposed registration method is based on the use of probabilistic correspondences between sparse image representations, leading to a robust handling of potentially missing correspondences. A maximum-a-posteriori framework is used to derive the optimization criterion with respect to deformation parameters that aim to compensate not only spatial differences between the images but also appearance differences. A multi-resolution scheme speeds-up the optimization and increases the robustness. The approach is compared to a state-of-theart intensity-based variational registration method using MR brain images. The comprehensive quantitative evaluation using images with simulated stroke lesions shows a significantly higher accuracy and robustness of the proposed approach.

关键词： Image registration 3D image processing expectation maximization algorithms Pathology Image segmentation Brain Medical imaging Statistical modeling

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Modeling and estimation of wavelet coefficients using elliptically-contoured Multivariate laplace vectors

Modeling and estimation of wavelet coefficients using ellipt...

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Conference on Wavelets XII

作者： Selesnick, Ivan W. Polytech Univ Brooklyn NY 11201 USA

ISBN: (纸本)9780819468499

In this paper, we are interested in modeling groups of wavelet coefficients using a zero-mean, elliptically contoured multivariate Laplace probability distribution function (pdf). Specifically, we are interested in the problem of estimating a d-point Laplace vector, s. in additive white Gaussian noise (AWGN), n, from an observation, y = s + n. In the scalar case (d = 1), the MAP and MMSE estimators are already known;and in the vector case (d > 1), the MAP estimator can be obtained by an iterative successive substitution algorithm. For the special case where the contour of the Laplace pdf is spherical, the MMSE estimators for the vector case (d > 1) have been derived in our previous work;we have shown that the MMSE estimator can be expressed in terms of the generalized incomplete Gamma function. For the general elliptically-contoured case, the MMSE estimator can not be expressed as such. In this paper, we therefore investigate approximations to the MMSE estimator of a Laplace vector in AWGN.

关键词： Wavelets expectation maximization algorithms Data modeling Convolution Signal to noise ratio Spherical lenses Denoising Numerical integration Bessel functions Wavelet transforms

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A preliminary investigation of using prior information for potentially improving image reconstruction in few-view CT

A preliminary investigation of using prior information for p...

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Medical Imaging 2008 Conference

作者： Cho, Seungryong Sidky, Emil Y. Bian, Junguo Pelizzari, Charles A. Pan, Xiaochuan Univ Chicago Dept Radiol Chicago IL 60637 USA Univ Chicago Dept Radiat & Cellular Oncol Chicago IL 60637 USA

ISBN: (纸本)9780819470973

There exists a strong need to reconstruct computed tomographic (CT) images with practically useful quality from a small number of projections in image-guided radiation therapy: for lowering radiation dose delivered to the subject, for shortening the imaging time, and for reducing the imaging-configuration complexity. We have recently developed an iterative image reconstruction algorithm based on total-variation (TV) minimization from incomplete projection data in CT. In numerical studies with a variety of incomplete projection-data sets including truncated data, reduced scan range, and sparse sampling, the developed algorithm seems to yield reasonable reconstruction, as compared to some of the existing algorithms, such as algebraic reconstruction technique (ART) and expectation minimization (EM). The TV-based algorithm begins in general with a uniform image as an initial guess, and goes through iteration steps to minimize the image TV subject to satisfying the given incomplete projection data. In image-guided radiation therapy (IGRT), a patient usually undergoes CT scanning for treatment. planning, which can provide the reference image for image guidance. Therefore, we propose a TV-based algorithm with a priori information in few-view CT for IGRT, in an attempt to further reduce the number of projections needed for image reconstruction from what the TV-based algorithm uses when no a priori information is included. In this work, we report the initial results of a preliminary numerical study that we have conducted to demonstrate this approach.

关键词： Reconstruction algorithms Computed tomography expectation maximization algorithms Image restoration Algorithm development Radiotherapy CT reconstruction Image quality Detection and tracking algorithms Image registration

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