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

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SOME ANALYTICAL AND STATISTICAL PROPERTIES OF FISHER INFORMATION

SOME ANALYTICAL AND STATISTICAL PROPERTIES OF FISHER INFORMA...

引用

CONF ON stochastic AND neural methods IN signal processing, image processing, AND COMPUTER VISION

作者： FRIEDEN, BR Optical Sciences Ctr./Univ. of Arizona (United States)

ISBN: (纸本)081940697X

The concept of Fisher information I is introduced. Smoothness properties of I, and its relation to entropy, disorder, and uncertainty are explored. Information I is generalized to N- component problems, and is expressed both in direct and Fourier spaces. Applications to ISAR radar imaging and to the derivation of physical laws are discussed.

关键词： Quantum mechanics Computer vision technology Error analysis image processing Machine vision Particles signal processing stochastic processes Statistical analysis Thermodynamics

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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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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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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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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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APPLICATION OF neural NETWORKS TO RANGE DOPPLER IMAGING

APPLICATION OF NEURAL NETWORKS TO RANGE DOPPLER IMAGING

引用

CONF ON stochastic AND neural methods IN signal processing, image processing, AND COMPUTER VISION

作者： WU, XQ ZHU, ZD Nanjing Aeronautical Institute (China)

ISBN: (纸本)081940697X

The use of neural networks are investigated for 2-D range Doppler microwave imaging. The range resolution of the microwave image is obtained by transmitting a wideband signal and the cross-range resolution is achieved by the Doppler frequency gradient in the same range bin. Hopfield neural networks are used to estimate the Doppler spectrum to enhance the cross- range resolution and reduce the processing time. There is a large number of neurons needed for the high cross-range resolution. In order to cut down the number of neurons, the reflectivities are replaced with their minimum norm estimates. The original Hopfield networks converge often to a local minina instead of the global minima. Simulated annealing is applied to control the gain of Hopfield networks to yield better convergence to the global minima. Results of imaging a model airplane from real microwave data are presented.

关键词： Neurons neural networks Doppler effect signal processing image processing Reflectivity image resolution Microwave radiation Radar Range imaging

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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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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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TARGET IDENTIFICATION BY MEANS OF ADAPTIVE neural NETWORKS IN THERMAL INFRARED imageS

TARGET IDENTIFICATION BY MEANS OF ADAPTIVE NEURAL NETWORKS I...

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CONF ON stochastic AND neural methods IN signal processing, image processing, AND COMPUTER VISION

作者： ACHEROY, M MEES, W Royal Military Academy (Belgium)

ISBN: (纸本)081940697X

A generic method for target recognition is presented. The stress is put on the methods based on the neural networks and more specifically on the adaptive resonance theory (ART) models. This type of artificial neural network (ANN) has the advantage of being unsupervised and adaptive: it is indeed able to acquire and adapt its long-term memory taking into account the context evolution. ART networks very quickly recognize classes that are already known, they also learn new images very fast. Two versions of ART are investigated: ART1, which only works with binary data, and ART2, which is working with analog data. In practice, ART1 seems to need larger images than ART2 to achieve the same efficiency, but is obviously faster. A preprocessor has been developed whose output is invariant to translation, rotation, and scale changes of the input. The most important feature of this preprocessor is its ability to preserve visual interpretation, which is not the case for the more classical methods using Fourier-like and log/polar transforms.

关键词： image processing Target recognition signal processing stochastic processes Visualization Computer vision technology image segmentation Machine vision Binary data Data modeling

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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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