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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 135 条记录，以下是51-60 订阅

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EVOLVING neural-NETWORK PATTERN CLASSIFIERS 2

EVOLVING NEURAL-NETWORK PATTERN CLASSIFIERS

引用

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

引用

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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neural and stochastic methods in image and signal processing II

Neural and Stochastic Methods in Image and Signal Processing...

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neural and stochastic methods in image and signal processing II 1993

The proceedings contain 29 papers. The topic discussed include: algorithm for classification of multispectral data and its implementation on a massively parallel computer;convergence measure and some parallel aspects of Markov-chain Monte Carlo algorithms;evidential reasoning based on Dempster-Shafer theory and its application to medical image analysis;cluster approximations for statistical image processing;image recovery and segmentation using competitive learning in a layered network;feature competition and domain of attraction in artificial-perceptron pattern recognizer;and probabilistic spectral feature extraction technique for neural networks.

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AN ALGORITHM FOR CLASSIFICATION OF MULTISPECTRAL DATA AND ITS IMPLEMENTATION ON A MASSIVELY-PARALLEL computer 2

AN ALGORITHM FOR CLASSIFICATION OF MULTISPECTRAL DATA AND IT...

引用

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

作者： SHAHSHAHANI, BM LANDGREBE, DA School Of Electrical Engineering Purdue University West Lafayette 47906 IN United States

ISBN: (纸本)0819412813

A new method for classification of multi-spectral data is proposed. This method is based on fitting mixtures of multivariate Gaussian components to training and unlabeled samples by using the EM algorithm. Through a backtracking search strategy with appropriate depth bounds, a series of mixture models are compared. The validity of the candidate models are evaluated by considering their description lengths and allocation rates. The most suitable model is selected and the multi-spectral data are classified accordingly. The EM algorithm is mapped onto a massively parallel computer system to reduce the computational cost. Experimental results show that the proposed algorithm is more robust against variations in training samples than the conventional supervised Gaussian maximum likelihood classifier. © 1993 SPIE. All rights reserved.

关键词： Mixtures

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methods for numerical integration of high-dimensional posterior densities with application to statistical image models

Methods for numerical integration of high-dimensional poster...

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neural and stochastic methods in image and signal processing II

作者： LaValle, Steven M. Moroney, Kenneth J. Hutchinson, Seth A.

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.

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Multiresolution statistical methods in image analysis

Multiresolution statistical methods in image analysis

引用

Intelligent Robots and computer vision XI: Biological, neural Net, and 3-D methods

作者： Luettgen, Mark R. Karl, William C. Willsky, Alan S. Tenney, Robert R. Massachusetts Inst. of Technology Cambridge MA USA

ISBN: (纸本)0819410276

In this paper, we discuss a statistical framework for multiscale signal and image processing based on a class of multiresolution stochastic models, which can be used to represent spatial random processes at a range of scales. The model class is quite rich, and in fact includes the class of Markov random fields. In addition, the models have a scale recursive structure which naturally leads to efficient, scale recursive algorithms for smoothing and likelihood calculation. We discuss an application of the framework to the problem of computing optical flow in image sequence, and demonstrate computational savings on the order of one to two orders of magnitude over standard algorithms.

关键词： image analysis

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A growing and splitting elastic network for vector quantization 3

A growing and splitting elastic network for vector quantizat...

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1993 3rd IEEE-SP Workshop on neural Networks for signal processing, NNSP 1993

作者： Fritzke, B. International Computer Science Institute 1947 Center Street BerkeleyCA94704-1105 United States

ISBN: (纸本)0780309286

A new vector quantization method is proposed which incrementally generates a suitable codebook. During the generation process, new vectors are inserted in areas of the input vector space where the quantization error is especially high. A one-dimensional topological neighborhood makes it possible to interpolate new vectors from existing ones. Vectors not contributing to error minimization are removed. After the desired number of vectors is reached, a stochastic approximation phase fine tunes the codebook. The final quality of the codebooks is exceptional. A comparison with two methods for vector quantization is performed by solving an image compression problem. The results indicate that the new method is clearly superior to both other approaches. © 1993 IEEE.

关键词： Vector quantization

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Practical, computer-aided registration of multiple, three-dimensional, magnetic-resonance observations of the human brain 2

Practical, computer-aided registration of multiple, three-di...

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neural and stochastic methods in image and signal processing II 1993

作者： Diegert, Carl Sanders, John A. Orrison, William W. Division 1424 Sandia National Laboratory Box 5800 AlbuquerqueNM87185 United States Department of Radiology University of New Mexico School of Medicine AlbuquerqueNM87131 United States Department of Radiology New Mexico Federal Regional Medical Center United States Department of Radiology and Neurology University of New Mexico School of Medicine AlbuquerqueNM87131 United States Department of Radiology Neurology Center for MEG New Mexico Federal Regional Medical Center United States

We define a methodology for aligning multiple, three-dimensional, magnetic-resonance observations of the human brain over six degrees of freedom. The observations may be taken with disparate resolutions, pulse sequences, and orientations. The alignment method is a practical combination of off-line and interactive computation. An off-line computation first automatically performs a robust surface extraction from each observation. Second, an operator executes interactively on a graphics workstation to produce the alignment. For our experiments, we were able to complete both alignment tasks interactively, due to the quick execution of our implementation of the off-line computation on a highly-parallel supercomputer. To assess accuracy of an alignment, we also propose a consistency measure.

关键词： Head Skin Visualization Ear Brain Computing systems Radiology Cameras Magnetic resonance imaging Natural surfaces

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