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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81-90 订阅

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SHAPE FROM FOCUS - SURFACE RECONSTRUCTION OF HYBRID SURFACES

SHAPE FROM FOCUS - SURFACE RECONSTRUCTION OF HYBRID SURFACES

引用

CONF ON stochastic and neural methods in signal processing, image processing, AND computer vision

作者： CHEN, SS TANG, WB XU, JH Univ. of North Carolina/Charlotte (United States) Fudan Univ. (China)

The shape-from-focus method uses different focus levels to obtain a sequence of object images and to estimate depth of image points. Recently, S. K. Nayar and Y. Nakagawa have treated the shape extraction problem of r... 详细信息

ISBN: (纸本)081940697X

关键词： image processing image analysis computer vision technology Filtering (signal processing) Machine vision signal processing stochastic processes image acquisition Sensors Visual process modeling

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APPLICATION OF neural NETWORK TO RESTORATION OF signalS DEGRADED BY A stochastic, SHIFT-VARIANT IMPULSE-RESPONSE FUNCTION AND ADDITIVE NOISE

APPLICATION OF NEURAL NETWORK TO RESTORATION OF SIGNALS DEGR...

引用

CONF ON stochastic and neural methods in signal processing, image processing, AND computer vision

作者： BILGEN, M HUNG, HS Iowa State Univ. (United States)

ISBN: (纸本)081940697X

An artificial neural network is adopted for estimating discrete (sampled in time and quantized in amplitude) signals degraded by a stochastic, shift-variant impulse response (blur) function in the presence of noise. The signal restoration problem is formulated as a combinatorial optimization problem wherein a nonlinear cost function, termed stochastic constrained restoration error energy, is to be minimized. By matching the cost function with the energy function of the associated neural network, the interconnection strengths and bias inputs of the neural network are related to the degraded signal, blur statistics, and constraint parameters. The solution which minimizes the energy function of the neural network is thus obtained iteratively by the simulated annealing algorithm. Simulation results show the effectiveness of the proposed algorithm which has, in addition, the capability of imposing level constraints on the original signal.

关键词： stochastic processes neural networks Algorithms Neurons Interference (communication) image processing computer vision technology Machine vision signal processing Data modeling

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GENERAL-METHOD FOR ACCELERATING SIMULATED ANNEALING ALGORITHMS FOR BAYESIAN image-RESTORATION

GENERAL-METHOD FOR ACCELERATING SIMULATED ANNEALING ALGORITH...

引用

CONF ON stochastic and neural methods in signal processing, image processing, AND computer vision

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

ISBN: (纸本)081940697X

A new stochastic technique is described for the Bayesian restoration of gray-level images corrupted by white noise. The proposed technique is related to simulated annealing but generates candidates more efficiently for gray-level images than either the Gibbs sampler or the Metropolis procedure. For a logarithmic cooling schedule, asymptotic convergence of the algorithm is proved by analyzing the corresponding inhomogeneous Markov chain. For an exponential cooling schedule, the new technique is shown experimentally to restore floating point images in 1/50 of the time required for the usual simulated annealing. Experimental restorations of gray-level images corrupted by white noise are presented.

关键词： stochastic processes Algorithms image processing image restoration Annealing computer vision technology Machine vision signal processing Process modeling signal to noise ratio

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APERTURE SYNTHESIS IN ASTRONOMICAL RADIO-INTERFEROMETRY USING MAXIMUM-ENTROPY ON THE MEAN

APERTURE SYNTHESIS IN ASTRONOMICAL RADIO-INTERFEROMETRY USIN...

引用

CONF ON stochastic and neural methods in signal processing, image processing, AND computer vision

作者： LEBESNERAIS, G NAVAZA, J DEMOMENT, G CNRS/Lab. des Signaux et Systemes (France) Lab. de Physique (France)

ISBN: (纸本)081940697X

A new algorithm for aperture synthesis in radio-astronomy is presented. It is based on the principle of the maximum entropy on the mean. The procedure jointly performs estimation of the unknown phase aberrations which composes the Fourier data and image reconstruction. It partly derives from a preexisting imaging method developed in the field of crystallography. A simulated example of the aperture synthesis indicates the efficiency of the method.

关键词： Antennas image restoration Reconstruction algorithms signal processing Synthetic apertures image processing Silicon computer vision technology Machine vision stochastic processes

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STATISTICAL image ALGEBRA - A BAYESIAN-APPROACH

STATISTICAL IMAGE ALGEBRA - A BAYESIAN-APPROACH

引用

CONF ON stochastic and neural methods in signal processing, image processing, AND computer vision

作者： DAVIDSON, JL CRESSIE, NAC Iowa State Univ. (United States)

ISBN: (纸本)081940697X

A mathematical structure used to express image processing transforms, the AFATL image algebra has proven itself useful in a wide variety of applications. The theoretical foundation for the image algebra includes many important constructs for handling a wide variety of image processing problems: questions relating to linear and nonlinear transforms, including decomposition techniques; mapping of transformations to computer architectures; neural networks; recursive transforms; and data manipulation on hexagonal arrays. However, statistical notions have been included only on a very elementary level and on a more sophisticated level in the literature. This paper presents an extension of the current image algebra that includes a Bayesian statistical approach. It is shown how images are modeled as random vectors, probability functions or mass functions are modeled as images, and conditional probability functions are modeled as templates. The remainder of the paper gives a brief discussion of the current image algebra, an example of the use of image algebra to express high-level image processing transforms, and the presentation of the statistical development of the image algebra.

关键词： image processing computer vision technology Machine vision Transform theory Binary data stochastic processes signal processing Convolution neural networks Artificial neural networks

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MARKOV RANDOM-FIELDS FOR TEXTURE CLASSIFICATION

MARKOV RANDOM-FIELDS FOR TEXTURE CLASSIFICATION

引用

CONF ON stochastic and neural methods in signal processing, image processing, AND computer vision

作者： CHEN, CC National Tsing Hua Univ. (Taiwan)

ISBN: (纸本)081940697X

Texture features obtained by fitting generalized Ising, auto-binomial, and Gaussian Markov random fields to homogeneous textures are evaluated and compared by visual examination and by standard pattern recognition methodology. The Markov random field model parameters capture the strong cues for human perception, such as directionality, coarseness, and/or contrast. The limited experiments for the classification of natural textures and sandpaper textures by using various classifiers suggest that both feature extraction and classifier design be carefully considered.

关键词： Feature extraction image classification computer vision technology image processing Machine vision signal processing stochastic processes Visual process modeling Classification systems Magnetorheological finishing

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TRANSFORMATION FROM TRISTIMULUS RGB TO MUNSELL NOTATION HVC IN A COLORED computer vision SYSTEM

TRANSFORMATION FROM TRISTIMULUS RGB TO MUNSELL NOTATION HVC ...

引用

CONF ON stochastic and neural methods in signal processing, image processing, AND computer vision

作者： JIN, GF ZHU, ZM YU, XG Tsinghua Univ. (China)

ISBN: (纸本)081940697X

The Transformation from tristimulus RGB to triattribute HVC ofhuman vision is an important problem in computer vision and CAD. The primary obstacle to finding a brief algorithm is the complicated case of Munsell loci appearing in the existing chromatic spaces. This article introduces the concept of fuzzy logic set into the computation and then handles Munsell system by fitting method so as to simplify the complex function. A better result has been achieved and such an approach has been satisfactorily used in a diagnosis system of tongue feacture in traditional Chinese medicine.

关键词： computer vision technology Machine vision RGB color model Computing systems Fuzzy logic image processing Medicine stochastic processes Colorimetry Tongue

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ON ERROR PROBABILITIES OF MINIMUM DISTANCE CLASSIFIERS

ON ERROR PROBABILITIES OF MINIMUM DISTANCE CLASSIFIERS

引用

CONF ON stochastic and neural methods in signal processing, image processing, AND computer vision

作者： POUBLAN, H CASTANIE, F ENSEEIHT/LEN7(GAPSE) 2 rue Camichel Toulouse 31071 France

ISBN: (纸本)081940697X

In the Gaussian case, the Bayes classifiers and the minimum distance classifiers are compared. The comparison is based on the error probability and on the bias introduced by the estimation of the law parameters. It is shown, both theoretically and by simulations, that even a suboptimal use of the minimum distance classifiers may be justified when the finite design sample size is small with regards to dimensionality. An application to signal classification is studied where the best modelisation of the signal is not the best representation for classification. © 1991 SPIE. All rights reserved.

关键词： stochastic systems

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NOVEL TRANSFORM FOR image DESCRIPTION AND COMPRESSION WITH IMPLEMENTATION BY neural ARCHITECTURES

NOVEL TRANSFORM FOR IMAGE DESCRIPTION AND COMPRESSION WITH I...

引用

CONF ON stochastic and neural methods in signal processing, image processing, AND computer vision

作者： BENARIE, J RAO, KR Illinois Institute of Technology (United States)

ISBN: (纸本)081940697X

A general method for signal representation using nonorthogonal basis functions that are composed of Gaussians are described. The Gaussians can be combined into groups with predetermined configuration that can approximate any desired basis function. The same configuration at different scales forms a set of self-similar wavelets. The general scheme is demonstrated by representing a natural signal employing an arbitrary basis function. The basic methodology is demonstrated by two novel schemes for efficient representation of 1-D and 2- D signals using Gaussian basis functions (BFs). Special methods are required here since the Gaussian functions are nonorthogonal. The first method employs a paradigm of maximum energy reduction interlaced with the A* heuristic search. The second method uses an adaptive lattice system to find the minimum-squared error of the BFs onto the signal, and a lateral-vertical suppression network to select the most efficient representation in terms of data compression.

关键词： image processing signal processing image compression Machine vision computer vision technology Wavelets stochastic processes Data compression signal to noise ratio Smoothing

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BAYESIAN-ESTIMATION OF SMOOTH OBJECT MOTION USING DATA FROM DIRECTION-SENSITIVE VELOCITY SENSORS

BAYESIAN-ESTIMATION OF SMOOTH OBJECT MOTION USING DATA FROM ...

引用

CONF ON stochastic and neural methods in signal processing, image processing, AND computer vision

作者： FONG, DY POMALAZARAEZ, CA Lockheed Palo Alto Research Labs. (United States) Purdue Univ. (United States)

ISBN: (纸本)081940697X

A two-stage process involving Bayesian estimates of smooth velocity vectors is used to detect physical object movements in an image sequence containing noisy background motions. This process computes the probability that a velocity vector is `smooth' with respect to a vector in the previous frame. Those vectors with a high probability are assembled into `paths' and paths longer than a threshold are retained. When this process is applied to the output of a velocity- sensitive network, random movements from the background are filtered out from the sequence, retaining only the smooth motion vectors.

关键词： Sensors image processing Neurons Motion estimation computer simulations computer vision technology Filtering (signal processing) Machine vision signal processing stochastic processes

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