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检索条件"任意字段=Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision 1991"

共 131 条记录，以下是41-50 订阅

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Recovering resolution and reducing noise in basis images via optimization methods using physical models 3

Recovering resolution and reducing noise in basis images via...

引用

neural and stochastic methods in image and signal processing III 1994

作者： Garnier, Stephen J. Bilbro, Griff L. Snyder, Wesley E. Department of Electrical and Computer Engineering North Carolina State University RaleighNC United States

This work addresses an optimization approach to sensor fusion and applies the technique to Magnetic Resonance image (MRI) restoration. Several images are related using a physical model ( spin equation) to corresponding basis images. The basis images (proton density and two nuclear relaxation times) are determined from the MRI data and subsequently used to obtain excellent restorations. The method also has been applied to image restoration problems in other domains. Magnetic Resonance images depend nonlinearly on proton density, ρ, two nuclear relaxation times, T1 and T2, and two control parameters, TE and TR. All images are modeled as Markov random fields. Four maximum a posteriori (MAP) restorations are presented, which reduce the correlated noise in the basis images, thereby reducing the noise in the restored MR images. The "product" and "sum" forms for basis (signal) and spatial correlations are discussed, compared and evaluated for various situations and features. A novel method of global optimization necessary for the nonlinear techniques is also introduced. The new global optimization method is related to mean field annealing but is better suited to this application. This approach to sensor fusion, using global optimization, Markov Random Field models, and Bayesian techniques, has been generalized and applied to other problem domains, such as the restoration of multiple-modality laser range and luminance signals. © 1994 SPIE.

关键词： image reconstruction

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methods FOR NUMERICAL-INTEGRATION OF HIGH-DIMENSIONAL POSTERIOR DENSITIES WITH APPLICATION TO STATISTICAL image-MODELS 2

METHODS FOR NUMERICAL-INTEGRATION OF HIGH-DIMENSIONAL POSTER...

引用

CONF ON neural AND stochastic methods IN image AND signal processing 2

作者： LAVALLE, SM MORONEY, KJ HUTCHINSON, SA Univ. of Illinois/Urbana-Champaign (United States)

ISBN: (纸本)0819412813

Numerical computation with Bayesian posterior densities has recently received much attention both in the statistics and computer vision communities. This paper explores the computation of marginal distributions for models that have been widely considered in computer vision. These computations can be used to assess homogeneity for segmentation, or can be used for model selection. In particular, we discuss computation methods that apply to a Markov random field formation, implicit polynomial surface models, and parametric polynomial surface models, and present some demonstrative experiments.

关键词： image segmentation Visual process modeling Data modeling Monte Carlo methods Statistical modeling Magnetorheological finishing Numerical integration Statistical analysis computer vision technology Machine vision

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ROBUST FRACTAL CHARACTERIZATION OF 1-D AND 2-D signalS 2

ROBUST FRACTAL CHARACTERIZATION OF 1-D AND 2-D SIGNALS

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CONF ON neural AND stochastic methods IN image AND signal processing 2

作者： AVADHANAM, N MITRA, S Texas Tech Univ. (United States)

ISBN: (纸本)0819412813

Fractal characterization of signals is well suited in analysis of some time series data and in classification of natural shapes and textures. A maximum likelihood estimator is used to measure the parameter H which is directly related to the fractal dimension. The robustness of the estimator and the performance of the method are demonstrated on datasets generated using a variety of techniques. Finally the characterization is used in segmentation of composite images of natural textures.

关键词： Fractal analysis image segmentation Composites Error analysis Statistical analysis image classification Polymers Edge detection computer vision technology Machine vision

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FAST stochastic GLOBAL OPTIMIZATION 2

FAST STOCHASTIC GLOBAL OPTIMIZATION

引用

CONF ON neural AND stochastic methods IN image AND signal processing 2

作者： BILBRO, GL North Carolina State Univ. (United States)

ISBN: (纸本)0819412813

A new stochastic optimization algorithm is introduced in which a pipeline of many biased stochastic procedures cooperate to concurrently sample the usual Boltzmann distribution for different temperatures. Convergence and efficiency of the pipeline algorithm is proved under certain conditions. Experimental confirmation is provided using seven standard test problems in nonlinear optimization.

关键词： stochastic processes Optimization (mathematics) Algorithms Algorithm development Astatine Detection and tracking algorithms signal processing computer engineering computer simulations Differential equations

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image COMPRESSION USING BOLTZMANN MACHINES 2

IMAGE COMPRESSION USING BOLTZMANN MACHINES

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CONF ON neural AND stochastic methods IN image AND signal processing 2

作者： LIU, Y Savannah State College (United States)

ISBN: (纸本)0819412813

In this paper, the idea of image compression using the Boltzmann machines will be developed. We will first introduces (theta) -transformation and show it is complete for a certain class of images. Then we will show that the Boltzmann machine can implement the first few terms in the (theta) -transformation; therefore, the Boltzmann machines can be used as image encoders. The compression ratio and distortion of this new image compression method are discussed.

关键词： image compression Neurons Distortion computer programming Data compression Fractal analysis Polonium neural networks Promethium stochastic processes

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CLUSTER APPROXIMATIONS FOR STATISTICAL image-processing 2

CLUSTER APPROXIMATIONS FOR STATISTICAL IMAGE-PROCESSING

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CONF ON neural AND stochastic methods IN image AND signal processing 2

作者： WU, CH DOERSCHUK, PC Purdue Univ. (Taiwan) Purdue Univ. (United States)

ISBN: (纸本)0819412813

A disadvantage of using discrete-state Markov random field models of images is that optimal estimators for reconstruction problems require excessive and typically random amounts of computation. In one approach the key task is the computation of the conditional mean of the field given the data or equivalently the unconditional mean of the a posteriori field. In this paper we describe a hierarchy of deterministic parallelizable methods for such computations.

关键词： image restoration image processing Binary data Magnetorheological finishing Performance modeling Radon computer simulations Crystallography X-rays Algorithms

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EVOLVING neural-NETWORK PATTERN CLASSIFIERS 2

EVOLVING NEURAL-NETWORK PATTERN CLASSIFIERS

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CONF ON neural AND stochastic methods IN image AND signal processing 2

作者： MCDONNELL, JR WAAGEN, DE PAGE, WC Naval Command Control and Ocean Surveillance Ctr. (United States)

ISBN: (纸本)0819412813

This work investigates the application of evolutionary programming for automatically configuring neural network architectures for pattern classification tasks. The evolutionary programming search procedure implements a parallel nonlinear regression technique and represents a powerful method for evaluating a multitude of neural network model hypotheses. The evolutionary programming search is augmented with the Solis & Wets random optimization method thereby maintaining the integrity of the stochastic search while taking into account empirical information about the response surface. A network architecture is proposed which is motivated by the structures generated in projection pursuit regression and the cascade-correlation learning architecture. Results are given for the 3-bit parity, normally distributed data, and the T-C classifier problems.

关键词： computer programming neural networks Network architectures stochastic processes Neurons Evolutionary optimization Autoregressive models image classification Brain mapping Chemical elements

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image RECOVERY AND SEGMENTATION USING COMPETITIVE LEARNING IN A LAYERED NETWORK 2

IMAGE RECOVERY AND SEGMENTATION USING COMPETITIVE LEARNING I...

引用

CONF ON neural AND stochastic methods IN image AND signal processing 2

作者： PHOHA, VV OLDHAM, WJB Univ. of Central Texas (United States) Texas Tech Univ. (United States)

ISBN: (纸本)0819412813

In this study, the principle of competitive learning is used to develop an iterative algorithm for image recovery and segmentation. Within the framework of Markov Random Fields, the image recovery problem is transformed to the problem of minimization of an energy function. A local update rule for each pixel point is then developed in a stepwise fashion and is shown to be a gradient descent rule for an associated global energy function. Relationship of the update rule to Kohonen's update rule is shown. Quantitative measures of edge preservation and edge enhancement for synthetic images are introduced. Simulation experiments using this algorithm on real and synthetic images show promising results on smoothing within regions and also on enhancing the boundaries. Restoration results computer favorably with recently published results using Markov Random Fields and mean field approximation.

关键词： image restoration Algorithm development image segmentation image processing algorithms and systems Magnetorheological finishing image enhancement image quality computer science Reconstruction algorithms Ions

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GLOBAL DYNAMICS OF WINNER-TAKE-ALL NETWORKS 2

GLOBAL DYNAMICS OF WINNER-TAKE-ALL NETWORKS

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CONF ON neural AND stochastic methods IN image AND signal processing 2

作者： ELFADEL, IM Massachusetts Institute of Technology (United States)

ISBN: (纸本)0819412813

In this paper, we study the global dynamics of winner-take-all (WTA) networks. These networks generalize Hopfield's networks to the case where competitive behavior is enforced within clusters of neurons while the interaction between clusters is modeled by cluster-to- cluster connectivity matrices. Under the assumption of intracluster and intercluster symmetric connectivity, we show the existence of Lyapunov functions that allow us to draw rigorous results about the long-term behavior for both the iterated-map and continuous-time dynamics of the WTA network. Specifically, we show that the attractors of the synchronous, iterated- map dynamics are either fixed points or limit cycles of period 2. Moreover, if the network connectivity matrix satisfies a weakened form of positive definiteness, limit cycles can be ruled out. Furthermore, we show that the attractors of the continuous-time dynamics are only fixed points for any connectivity matrix. Finally, we generalize the WTA dynamics to distributed networks of clustered neurons where the only requirement is that the input-output mapping of each cluster be the gradient map of a convex potential.

关键词： Analog electronics Fourier transforms Dynamical systems Binary data Neurons Brain mapping Matrices Very large scale integration computer vision technology Machine vision

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A CONVERGENCE MEASURE AND SOME PARALLEL ASPECTS OF MARKOV-CHAIN MONTE-CARLO ALGORITHMS 2

A CONVERGENCE MEASURE AND SOME PARALLEL ASPECTS OF MARKOV-CH...

引用

CONF ON neural AND stochastic methods IN image AND signal processing 2

作者： MALFAIT, M ROOSE, D VANDERMEULEN, D K.U.Leuven Department of Computer Science Celestijnenlaan 200A Heverlee 3001 Belgium K.U.Leuven Interdisciplinary Research Unit for Radiological Imaging (ESAT + Radiology) Kard. Mercierlaan 94 Heverlee 3001 Belgium

ISBN: (纸本)0819412813

We examine methods to assess the convergence of Markov chain Monte Carlo (MCMC) algorithms and to accelerate their execution via parallel computing. We propose a convergence measure based on the deviations between simultaneously running MCMC algorithms. We also examine the acceleration of MCMC algorithms when independent parallel samplers are used and report on some experiments with coupled samplers. As applications we use small Ising model simulations and a larger medical image processing algorithm. © 1993 SPIE. All rights reserved.

关键词： Ising model

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