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检索条件"任意字段=Neural and Stochastic Methods in Image and Signal Processing II"

共 540 条记录，以下是361-370 订阅

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KNOWLEDGE-DIRECTED EEG signal ANALYSIS WITH RECURRENT CONTEXT-LEARNING neural NETWORKS 3

KNOWLEDGE-DIRECTED EEG SIGNAL ANALYSIS WITH RECURRENT CONTEX...

引用

Conference on neural and stochastic methods in image and signal processing iiI

作者： FU, LM UNIV FLORIDA DEPT COMP & INFORMAT SCIGAINESVILLEFL 32611

ISBN: (纸本)0819416282

EEG signal analysis is a key to the understanding of brain activities. Traditionally, this process involves quantifying the signal in terms of frequency and amplitude, on which basis a number of waveforms have been identified. The complexity of EEG signals warrants the construction of a computer program for automatic interpretation. Symbolic knowledge is being built up for correlating the quantity of certain waveforms and brain behavior, and this knowledge can be readily programmed into a knowledge-based system (expert system) for various purposes such as cognitive research, neurological evaluation, and clinical diagnosis. The presented approach employs a knowledge-based neural network in conjunction with a recurrent neural network model as a memory device which conducts context processing. This research emphasizes the need for the exploitation of "knowledge" and "context" in signal analysis. © 1994 SPIE.

关键词： EEG CONTEXT KNOWLEDGE-BASED neural NETWORKS PATTERN ANALYSIS

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A UNIFICATION OF SEVERAL image COMPRESSION methods 4

A UNIFICATION OF SEVERAL IMAGE COMPRESSION METHODS

引用

4th Conference on Hybrid image and signal processing

作者： LIU, Y SAVANNAH STATE COLL DEPT MATH & COMP SCISAVANNAHGA 31404

In this paper, several image compression theories are unified. These theories are: Hilbert image compression, Fractal image compression, image compression using Boltzmann machines, using stochastic artificial neural n... 详细信息

ISBN: (纸本)0819415421

关键词： image compression

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A stochastic APPROACH FOR MOTION VECTOR ESTIMATION IN VIDEO CODING 3

A STOCHASTIC APPROACH FOR MOTION VECTOR ESTIMATION IN VIDEO ...

引用

Conference on neural and stochastic methods in image and signal processing iiI

作者： KIM, SG KUO, CCJ UNIV SO CALIF DEPT ELECT ENGN SYSTINST SIGNAL & IMAGE PROCLOS ANGELESCA 90089

ISBN: (纸本)0819416282

Motion vector (MV) estimation plays an important role in motion compensated video coding. We propose in this research a new fast MV estimation algorithm by using a statistical approach. Our algorithm consists of two components. First, instead of using a fixed search window centered at the origin, we allow the center and the size of the search window to vary according to the motion vectors obtained in coding previous frames. Second, instead of performing an exhaustive block matching within the search window, we introduce a probability density function which has a peak at the window center and decays as the distance becomes larger, and generate a few search positions according to the probability density. Experiments are performed to illustrate the excellent performance of the proposed algorithm. © 1994 SPIE.

关键词： image coding

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image CLASSIFICATION BY CLASSIFIER COMBINING TECHNIQUE 3

IMAGE CLASSIFICATION BY CLASSIFIER COMBINING TECHNIQUE

引用

Conference on neural and stochastic methods in image and signal processing iiI

作者： LIU, K HUANG, YS SUEN, CY CONCORDIA UNIV CTR PATTERN RECOGNIT & MACHINE INTELLIGENCEMONTREAL H3G 1M8QUEBECCANADA

ISBN: (纸本)0819416282

A classification method has been proposed to recognize images of multiple classes based on algebraic feature extraction and classifier combining techniques. First, an image algebraic feature extraction method is applied to all pairs of classes to extract the image features. Then, a nearest neighbor classifier with a small number of prototypes is designed for each pair of classes based on the algebraic features of training samples. Finally, a neural network technique is used to combine the measurement values of paired classes. Experiments on the U.S. zip code data base show that the method is effective.

关键词： COMBINATION OF PATTERN CLASSIFIERS OPTIMAL DISCRIMINANT ANALYSIS FEATURE EXTRACTION CHARACTER RECOGNITION CLASSIFIER DESIGN neural NETWORK

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EVOLUTIONARY OPTIMIZATION OF CASCADED-NETWORKS 3

EVOLUTIONARY OPTIMIZATION OF CASCADED-NETWORKS

引用

Conference on neural and stochastic methods in image and signal processing iiI

作者： MCDONNELL, JR WAAGEN, DE NCCOSC RDT&E DIVSAN DIEGOCA 92152

ISBN: (纸本)0819416282

This work investigates the application of evolutionary search to cascade-correlation learning architectures. Evolutionary programming is used to generate the hidden weights of each candidate hidden unit in the cascade-correlation learning paradigm. The output weights are adapted using deterministic techniques. Evolutionary search is also used to modify the connectivity of each candidate unit so that parsimonious structures may be generated during the neural network construction process. This approach is appealing from a computational perspective since only a population of hidden nodes is being optimized as opposed to a population of neural networks. Results are given for selected low-dimensional examples.

关键词： neural NETWORK EVOLUTIONARY OPTIMIZATION CASCADE-CORRELATION CLASSIFIER DESIGN

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HIERARCHICAL MARKOV RANDOM-FIELDS WITH STABLE POINTS 3

HIERARCHICAL MARKOV RANDOM-FIELDS WITH STABLE POINTS

引用

Conference on neural and stochastic methods in image and signal processing iiI

作者： BUDZBAN, G STIREWALT, T SO ILLINOIS UNIV DEPT MATHCARBONDALEIL 62901

ISBN: (纸本)0819416282

Markov random field techniques for region labeling have become prevalent in image processing research since the seminal work of Geman and Geman in the early 1 980's. Their use in actual working systems, however, has been hampered by a number ofdifficult problems. Perhaps the most intractable of the problems has been the convergence rate of the algorithm. In this paper, we present a technique that introduces stable points in the labeling array of the random field. The stable points are determined by using a simple statistical pixel classifier together with a confidencemeasure at each pixel. The most confident (top 1% )pixellabels are selected and these labels are used to initiate the evolution of the random field. The stable points introduce pockets of "certainty" in the evolution of the process. The labeling is locally stable and even small numbers of stable points vastly decrease convergence rates of the algorithm.

关键词： Algorithms Magnetorheological finishing Computing systems stochastic processes Machine vision Computer vision technology Detection and tracking algorithms image filtering Probability theory Systems modeling

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GENETIC ALGORITHM FOR MAXIMUM-ENTROPY image-RESTORATION 3

GENETIC ALGORITHM FOR MAXIMUM-ENTROPY IMAGE-RESTORATION

引用

Conference on neural and stochastic methods in image and signal processing iiI

作者： TOMA, CE DATCU, MP EINDHOVEN UNIV TECHNOL DEPT MATH & COMP SCI5600 MB EINDHOVENNETHERLANDS

ISBN: (纸本)0819416282

Linear image restoration techniques induce erroneous detail around sharp intensity changes. Thus, considerable work has centered on nonlinear methods, which incorporate constraints to reduce the artifacts generated in the restoration. In our paper, we examine the applicability of genetic algorithms to solving optimization problems posed by nonlinear image recovery techniques, particularly by maximum entropy restoration. Each point in the solution space is a feasible image, with the pixels as decision variables. Search is multiobjective: the entropy of the estimate must be maximized, subject to constraints dependent on the observed data and image degradation model. We use Pareto techniques to achieve this combined requirement, and problem-oriented knowledge to direct the search. Typical issues for genetic algorithms are addressed: chromosomal representation, genetic operators, selection scheme, and initialization.

关键词： MAXIMUM ENTROPY image RESTORATION GENETIC ALGORITHMS MULTIOBJECTIVE SEARCH PARETO-OPTIMALITY PROBLEM-ORIENTED GENETIC OPERATORS ELITISM

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VARIATIONAL VERSUS MARKOV RANDOM-FIELD methods FOR image SEGMENTATION 3

VARIATIONAL VERSUS MARKOV RANDOM-FIELD METHODS FOR IMAGE SEG...

引用

Conference on neural and stochastic methods in image and signal processing iiI

作者： KULKARNI, SR MITTER, SK PRINCETON UNIV DEPT ELECT ENGNPRINCETONNJ 08544

ISBN: (纸本)0819416282

Variational and Markov random field (MRF) methods have been proposed for a number of tasks in image processing and early vision. Continuous (variational) formulations have the advantages of being more amenable to analysis and more easily incorporating geometric constraints and invariants. However, discrete (MRF) formulations have computational advantages and are typically used in implementing such methods. Certain commonly used MRF models for image segmentation do not properly approximate a standard continuous formulation in the sense that the discrete solutions may not converge to a solution of the continuous problem as the lattice spacing tends to zero. We propose several modifications of the MRF formulations for which we prove convergence in the continuum limit. Although these MRF models require complex neighborhood structures, we discuss results that indicate that for MRF models with bounded number of states, the difficulties are inherent and cannot be avoided in any scheme with the desired convergence properties.

关键词： image SEGMENTATION RESTORATION VARIATIONAL MARKOV RANDOM FIELD REGULARIZATION

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neural-NETWORK ARCHITECTURE FOR OBJECT RECOGNITION 3

NEURAL-NETWORK ARCHITECTURE FOR OBJECT RECOGNITION

引用

Conference on neural and stochastic methods in image and signal processing iiI

作者： PENG, MK NAPIER UNIV DEPT ELECTEDINBURGH EH14 1DJSCOTLAND

ISBN: (纸本)0819416282

Model-based 2-D object recognition is investigated by using neural network. Object recognition is treated as a subgraph matching. A neural network system is proposed to complete subgraph matching. The system consists of a large Hopfield network, called global network, and several small Hopfield networks, called subnetworks. The system starts with a randomly set initial state of the global network. The subnetworks are dynamically created based on the stable output state of the global network and then the outputs of the subnetworks are feedbacked to the global network to reset its initial state. This process continues until the whole system is stabilized, where the optimal subgraph matching is obtained. This method avoids the local minimum problem from using a single Hopfield network and also uses much less calculating time than simulated annealing algorithm. Computer simulation is done to verify it.

关键词： neural NETWORK HOPFIELD NETWORK OBJECT RECOGNITION SUBGRAPH MATCHING

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FAST FRACTAL FEATURE-EXTRACTION FOR TEXTURE SEGMENTATION USING WAVELETS 3

FAST FRACTAL FEATURE-EXTRACTION FOR TEXTURE SEGMENTATION USI...

引用

Conference on neural and stochastic methods in image and signal processing iiI

作者： KAPLAN, LM KUO, CCJ UNIV SO CALIF DEPT ELECT ENGN SYSTINST SIGNAL & IMAGE PROCLOS ANGELESCA 90089

ISBN: (纸本)0819416282

In this work, we use the 1D Haar transform fractal estimation algorithm to calculate the local fractal dimension estimates of 2D texture data. The new algorithm provides directed fractal dimension estimates which are used as features for texture segmentation. The method is fast due to the pyramid structure of the Haar transform and nearly optimal in the maximum likelihood sense for fBm data. We compare the low complexity of this new algorithm with the complexity of existing fractal feature extraction techniques, and test our new method on fBm data and real Brodatz textures.

关键词： FRACTALS FRACTIONAL BROWNIAN MOTION TEXTURE SEGMENTATION WAVELETS

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