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检索条件"主题词=Expectation maximization algorithms"

共 128 条记录，以下是111-120 订阅

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QUADTREE-STRUCTURED MULTIPLE RESOLUTION SEGMENTATION OF SOME COMPUTED IMAGES 15

QUADTREE-STRUCTURED MULTIPLE RESOLUTION SEGMENTATION OF SOME...

引用

CONF ON APPLICATIONS OF DIGITAL IMAGE PROCESSING 15

作者： LEI, TH ZHAO, Z Univ. of Maryland/Baltimore (United States)

ISBN: (纸本)0819409448

Image statistics of some computed images and statistical history of the corresponding quadtree are analyzed. A new multiple resolution segmentation (MRS) approach using quadtree for these computed images is presented. The results obtained by using this approach demonstrate the correctness of the derived statistical properties and efficacy of this MRS scheme.

关键词： Image segmentation Magnetic resonance imaging X-ray computed tomography Image resolution X-ray imaging X-rays Digital image processing Statistical analysis expectation maximization algorithms Image analysis

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REGULARIZED MULTIFRAME RESTORATION OF HUBBLE SPACE AND GROUND-BASED TELESCOPE IMAGES WITH PARALLEL IMPLEMENTATION 15

REGULARIZED MULTIFRAME RESTORATION OF HUBBLE SPACE AND GROUN...

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CONF ON APPLICATIONS OF DIGITAL IMAGE PROCESSING 15

作者： KLAUS, R BURKHARDT, H Technische Univ. Hamburg-Harburg (Germany)

ISBN: (纸本)0819409448

The spherical aberration of the primary mirror of the Hubble Space Telescope (HST) has seriously reduced the sensitivity and degraded the resolution of images from the instruments. After the first period of observation, most of the astronomic researchers do not try to make photometric measurements on original or somewhat restored images, but they see the opportunity to take the better resolution of the HST to upgrade their ground-based images. From that point of view, the need of multiframe restoration methods appeared. This paper first of all shows the problem of multiframe image restoration methods by looking at several well known single-frame methods and their generalization. The problem of week radiation in astronomical images is modelled by poisson processes which is well known from positron emission tomography. The unconstrained maximum likelihood method for this class of problems leads to an equation that can be solved by an expectation maximization type iterative algorithm. A regularizing subband method is proposed that incorporates the multiframe problem in a single-frame scheme by a subband type solution. The filtering for the subbands incorporates the model inherent freedom. Taking the simplest case of ideal low- and high-pass filters, there is still the choise of the cut-off frequency. The low frequency band is taken from the ground-based telescope with its good photometric property, the high frequency band is taken from the HST with its good resolution property. A transputer based parallel implementation with an underlying message passing system will be shown as a time efficient solution of the algorithms.

关键词： Point spread functions Space telescopes Telescopes expectation maximization algorithms Filtering (signal processing) Image processing Image restoration Image resolution Digital image processing Hubble Space Telescope

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TRAINING A BOLTZMANN MACHINE FOR EDGE-PRESERVING IMAGE-RESTORATION 2

TRAINING A BOLTZMANN MACHINE FOR EDGE-PRESERVING IMAGE-RESTO...

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CONF ON NEURAL AND STOCHASTIC METHODS IN IMAGE AND SIGNAL PROCESSING 2

作者： BEDINI, L PANDOLFI, S TONAZZINI, A Istituto di Elaborazione della Informazione (Italy)

ISBN: (纸本)0819412813

The most successful methods to stabilize inverse ill-posed problems in visual reconstruction use a priori information on the local regularity of the image as well as constraints on the geometry of the discontinuities. A commonly used method to incorporate prior knowledge into the problem is to adopt a Bayesian approach in which the image is modelled by a parametric Gibbs prior and the solution is obtained by minimizing the resulting posterior energy function (MAP estimate). However, this approach presents two major difficulties: the first is related to the non-convexity of the function to be optimized; the second to the choice of the model parameters that best fit the available prior knowledge. Since these parameters strongly affect the quality of the reconstructions, their selection is a critical task. They are usually determined empirically by trial and error. The paper proposes a generalized Boltzmann Machine which makes it possible to learn the most appropriate parameters for a given class of images from a series of examples. The trained Boltzmann Machine is then used to implement an annealing scheme for the minimization of the non-convex posterior energy. The method is applied to the restoration of piecewise smooth images.

关键词： Neurons Chemical elements Image restoration Stochastic processes expectation maximization algorithms Signal processing Binary data Magnetorheological finishing Annealing Visualization

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ESTIMATION-THEORETIC APPROACH TO THE DECONVOLUTION OF ATMOSPHERICALLY DEGRADED IMAGES WITH WAVE-FRONT SENSOR MEASUREMENTS 2

ESTIMATION-THEORETIC APPROACH TO THE DECONVOLUTION OF ATMOSP...

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Conference on Digital Image Recovery and Synthesis II

作者： SCHULZ, TJ MICHIGAN TECHNOL UNIV DEPT ELECT ENGNHOUGHTONMI 49931

ISBN: (纸本)0819412783

Deconvolution from wavefront sensing (or self-referenced speckle holography) has previously been proposed as a post-detection processing technique for correcting turbulence-induced wavefront phase-errors in incoherent imaging systems. In this paper, a new methodology is considered for processing the image and wavefront-sensor data in which the method of maximum-likelihood estimation is used to simultaneously estimate the object intensity and phase errors directly from the detected images and wavefront-sensor data. This technique is demonstrated to work well in a situation for which the wavefront sensor's lenslet diameters are such that their images are not simply spots of light translated according to the local slope of the phase errors, but are instead an array of small, interfering speckle patterns.

关键词： Error analysis Telescopes Wavefront sensors expectation maximization algorithms Data modeling Deconvolution Image processing Image restoration Wavefronts Staring arrays

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POISSON MAP SUPERRESOLUTION ESTIMATOR WITH SMOOTHNESS CONSTRAINT 2

POISSON MAP SUPERRESOLUTION ESTIMATOR WITH SMOOTHNESS CONSTR...

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CONF ON NEURAL AND STOCHASTIC METHODS IN IMAGE AND SIGNAL PROCESSING 2

作者： SEMENTILLI, PJ NADAR, MS HUNT, BR Univ. of Arizona (United States)

ISBN: (纸本)0819412813

A well known problem associated with super-resolution of imagery is the introduction of oscillatory artifacts into the super-resolved object estimate. In this paper, we derive a Maximum A-Posteriori (MAP) object estimator subject to a constraint on the norm of the first differences of adjacent object pixels. The posterior density is derived from a Poisson observation model and a Poisson prior for the object. It is shown that this optimization problem is identical to a MAP estimator with a Markov Random Field (MRF) prior using zero- and first-order neighborhood cliques. While most MRF priors used for image restoration neglect the singleton clique, our model incorporates this additional a-priori object knowledge into the estimator. An iterative solution to the optimization problem is developed using the Picard iteration technique. Results are presented which demonstrate substantial artifact reduction while achieving the bandwidth extension necessary to accomplish super-resolution. Test cases include extended objects and natural scenes.

关键词： expectation maximization algorithms Super resolution Algorithm development Image restoration Magnetorheological finishing algorithms Convolution Image processing Optimization (mathematics) Imaging systems

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MULTISCALE STOCHASTIC APPROACH TO OBJECT DETECTION

MULTISCALE STOCHASTIC APPROACH TO OBJECT DETECTION

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Meeting on Visual Communications and Image Communication 93

作者： TRETTER, D BOUMAN, CA PURDUE UNIV W LAFAYETTEIN 47907

ISBN: (纸本)0819413690

We present a method for object detection based on a novel multiscale stochastic model together with Bayesian estimation techniques. This approach results in a fast, general algorithm which may be easily trained for specific objects. The object model is based on a stochastic tree structure in which each node is an important subassembly of the three dimensional object. Each node or subassembly is modeled using a Gaussian pyramid decomposition. The objective of the algorithm is then to estimate the unknown position of each subassembly, and to determine on the presence of the object. We use a fast multiscale search technique to compute the sequential MAP (SMAP) estimate of the unknown position, scale factor, and 2-D rotation for each subassembly. The search is carried out in a manner similar to a sequential likelihood ratio test, where the process advances in scale rather than time. We use a similar search to estimate the model parameters for a given object from a set of training images.

关键词： Stochastic processes Image resolution 3D modeling Data modeling expectation maximization algorithms Detection and tracking algorithms Image processing Error analysis Polonium Object recognition

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MODEL ADAPTIVE OPTIMAL IMAGE-RESTORATION 15

MODEL ADAPTIVE OPTIMAL IMAGE-RESTORATION

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CONF ON APPLICATIONS OF DIGITAL IMAGE PROCESSING 15

作者： JEFFS, BD PUN, WH Brigham Young Univ. (United States)

ISBN: (纸本)0819409448

This work addresses the problem of restoring blurred and noise corrupted images when typical deterministic methods (least squares, max entropy, etc.) are not known to be optimal. The proposed approach is to adapt, based on observed image data only, the optimization criterion used in the restoration to one most suited to the statistical properties of the observed image. This is done without prior knowledge or restriction assumptions about the data. Maximum likelihood (ML) image restoration is considered where the noise distribution is not known ar-priori, but is modeled by a general family of parametric distributions whose widely varying shapes are controlled by a small set of parameters. It is shown that the generalized p-Gaussian (gpG) distribution family can match a surprisingly wide range of typical noise distributions (uniform, Gaussian, exponential, Cauchy, etc.) by varying a single shape parameter p. Restoration is accomplished by adapting the noise model through adjusting p as part of the estimation problem. Once p is found, the ML estimate is simply the associated lp norm minimization solution. The optimization criterion is thus adapted to suit the observation. Examples of improved reconstruction using this method, as compared with least squares and maximum entropy, are presented. The extension of model adaptive restoration to maximum a-posteriori (MAP) estimation is discussed. The potential applicability of another more general parametric distribution, the generalized beta of the second kind (GB2), is discussed.

关键词： Image restoration Stars Digital image processing Image processing Reconstruction algorithms Optimization (mathematics) expectation maximization algorithms Statistical analysis Data modeling Signal to noise ratio

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SYSTEMATIC CONSTRUCTION OF ORDER-RECURSIVE LS ESTIMATION algorithms WITH ELEMENTARY ORTHOGONAL TRANSFORMATIONS 4

SYSTEMATIC CONSTRUCTION OF ORDER-RECURSIVE LS ESTIMATION ALG...

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4TH CONF ON ADVANCED SIGNAL PROCESSING algorithms, ARCHITECTURES, AND IMPLEMENTATIONS

作者： LING, F Motorola (United States)

ISBN: (纸本)0819412767

This paper demonstrates that order-recursive least squares (ORLS) algorithms based on orthogonal transformations and hyperbolic transformations can be systematically constructed in two steps. The first step is to determine the structure of the ORLS algorithm according to the property of the data vector in the LS estimation and the requirements to the output. The second step is to determine the proper implementation of building blocks of the ORLS structure using orthogonal or hyperbolic transformations. The canonical ORLS structure and some possible orthogonal/hyperbolic implementations of their building blocks are presented. It is also shown that some of the orthogonal transformations are only applicable to certain types of ORLS structures and not to others.

关键词： Algorithm development Error analysis Detection and tracking algorithms Digital signal processing expectation maximization algorithms Statistical analysis Factor analysis Magnesium Roads Telecommunications

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BLUR IDENTIFICATION AND IMAGE-RESTORATION WITH THE expectation maximization ALGORITHM 4

BLUR IDENTIFICATION AND IMAGE-RESTORATION WITH THE EXPECTATI...

引用

CONF ON PROPAGATION ENGINEERING

作者： DURACK, DL U.S. Army Atmospheric Sciences Lab. and New Mexico State Univ. (United States)

ISBN: (纸本)0819405965

The iterative expectation-maximization (EM) algorithm for identifying unknown blur, noise, and image parameters under Gaussian modeling assumptions is described. A new form of the equations updating parameter estimates is given, from which convergence conditions and symmetry properties of the parameter estimates are derived. The frequency domain resolution defined by the digital image is not appropriate for accurate parameter estimation. Instead, a version of the EM algorithm with frequency resolution appropriate for the blur point spread function (PSF) is proposed. Results are presented from a test of the reduced resolution algorithm, in which the importance of the initial PSF is studied.

关键词： expectation maximization algorithms Point spread functions Image restoration Turbulence Digital imaging Optical transfer functions Image resolution Statistical analysis Diffraction Signal to noise ratio

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SIGNAL-PROCESSING WITH RADIAL BASIS FUNCTION NETWORKS USING expectation maximization ALGORITHM CLUSTERING

SIGNAL-PROCESSING WITH RADIAL BASIS FUNCTION NETWORKS USING ...

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CONF ON ADAPTIVE SIGNAL PROCESSING

作者： UKRAINEC, A HAYKIN, S McMaster Univ. (Canada)

ISBN: (纸本)0819406937

Several researchers have investigated the usefulness ofradial basis function (RBF) neural networks for prediction of difficult (chaotic) time series.18 This paper demonstrates adaptive unsupervised / supervised learning procedures which improve on the RBF network performance in terms of generalization, and final mean-square error. After a description of the RBF network architecture, the learning of the network is discussed with comments on the advantages and disadvantages of various approaches. Unsupervised clustering of RBF centres has been used by researchers6'° to overcome some of the disadvantages in supervised learning, and to improve the performance of the RBF network while keeping the number of basis functions relatively low. A maximum likelihood solution for unsupervised clustering called the expectation maximization (EM) algorithm has recently been used'° to estimate the parameters of the RBF hidden layer units. The input density is modeled as a mixture of component gaussian distributions. The estimated parameters of the mixture density are then transplanted into the RBF network, after which supervised learning of the network takes place. An extension to input—output space clustering is shown to be superior. Although papers have been written on input space clustering for RBF networks, this paper examines why the extended metric clustering should be a superior methodology. Two examples, a chaotic time series predictor and a cross—polarization canceller, illustrate the performance of the algorithms. The EM algorithm with extended metric clustering method achieves superior performance on signal processing problems by creating a better hidden layer representation in areas of the input space where samples are more likely to be jointly located.

关键词： expectation maximization algorithms Signal processing Network architectures Radar Associative arrays Machine learning Detection and tracking algorithms Digital filtering Nonlinear optics Polarization

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