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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71-80 订阅

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computer simulation of turbulent jet structure radiography

Computer simulation of turbulent jet structure radiography

引用

neural and stochastic methods in image and signal processing

作者： Kodimer, Kory A. Parnell, Lynn A. Nelson, Robert S. Papin, Patrick J. Naval Command Control and Ocean Surveillance Cent. San Diego CA USA

ISBN: (纸本)0819409391

Liquid metal combustion chambers are under consideration as power sources for propulsion devices used in undersea vehicles. Characteristics of the reactive jet are studied to gain information about the internal combustion phenomena, including temporal and spatial variation of the jet flame, and the effects of phase changes on both the combustion and imaging processes. A ray tracing program which employs simplified Monte Carlo methods has been developed for use as a predictive tool for radiographic imaging of closed liquid metal combustors. A complex focal spot is characterized by either a monochromatic or polychromatic emission spectrum. For the simplest case, the x-ray detection system is modeled by an integrating planar detector having 100% efficiency. Several simple geometrical shapes are used to simulate jet structures contained within the combustor, such as cylinders, paraboloids, and ellipsoids. The results of the simulation and real time radiographic images are presented and discussed.

关键词： image processing

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Criterion for correct recalls in associative-memory neural networks

Criterion for correct recalls in associative-memory neural n...

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neural and stochastic methods in image and signal processing

作者： Ji, Han-Bing Shanghai Jiao Tong Univ. Shanghai China

ISBN: (纸本)0819409391

A novel weighted outer-product learning (WOPL) scheme for associative memory neural networks (AMNNs) is presented. In the scheme, each fundamental memory is allocated a learning weight to direct its correct recall. Both the Hopfield and multiple training models are instances of the WOPL model with certain sets of learning weights. A necessary condition of choosing learning weights for the convergence property of the WOPL model is obtained through neural dynamics. A criterion for choosing learning weights for correct associative recalls of the fundamental memories is proposed. In this paper, an important parameter called signal to noise ratio gain (SNRG) is devised, and it is found out empirically that SNRGs have their own threshold values which means that any fundamental memory can be correctly recalled when its corresponding SNRG is greater than or equal to its threshold value. Furthermore, a theorem is given and some theoretical results on the conditions of SNRGs and learning weights for good associative recall performance of the WOPL model are accordingly obtained. In principle, when all SNRGs or learning weights chosen satisfy the theoretically obtained conditions, the asymptotic storage capacity of the WOPL model will grow at the greatest rate under certain known stochastic meaning for AMNNs, and thus the WOPL model can achieve correct recalls for all fundamental memories. The representative computer simulations confirm the criterion and theoretical analysis.

关键词： neural networks

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Local estimation of visual signal translation using modulated wavelet transforms

Local estimation of visual signal translation using modulate...

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neural and stochastic methods in image and signal processing

作者： Eagleson, Roy A. Univ. of Western Ontario London Ontario Canada

ISBN: (纸本)0819409391

In computational perception, `visual motion analysis' is most commonly identified with the problem of measuring the infinitesimal rate of translation at various local spatial neighborhoods in a time-varying signal. Many problems associated with measuring these motion vectors can be addressed by considering the following simplified one-dimensional case. Given two samples, an original function fo(x), and another sample ft(x) taken momentarily afterwards;compute the translation parameter τ which provides a best-fit for the transformation model, Tτ : fo(x) &rarr ft(x) = fo(x + τ) over some finite local region. The `goodness' of this fit requires evaluation by a suitable performance metric since measurement uncertainty and added noise will corrupt the solution of τ. This error can be reduced if the measurement is supported by a wider spatial region. However, the `pure translation' model is usually only valid within some small local neighborhood. These two competing constraints inherently compromise the measurement process. In this paper, a new technique is developed for estimating this translation parameter using a localized (`wavelet') representation, and it provides a measure of the uncertainty of the resulting estimate. In addition, a trade-off is identified between the local neighborhood width and the uncertainty of the translation estimate. It is similar to the well-known Heisenberg uncertainty principle: The product of the variances of the uncertainty of position and translation is bounded below by a finite constant.

关键词： computer vision

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Kohonen neural network for image coding based on iteration transformation theory

Kohonen neural network for image coding based on iteration t...

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neural and stochastic methods in image and signal processing

作者： Bogdan, Alexandru Meadows, H.E. Columbia Univ. New York NY USA

ISBN: (纸本)0819409391

Iterated transformation theory (ITT), also known as fractal coding, is a relatively new block compression method which removes redundancies between different scale representations of the uncompressed signal. In ITT coding we are looking for a piecewise continuous mapping from the space of all images with the same support onto itself which has a close approximation of the desired image as a unique fixed point. The mapping is then the code for the image, and for decoding we iterate the mapping on any initial image, orders of magnitude faster than encoding. We have reduced the computational load of finding the piecewise continuous transformation by using a self-organizing feature map (SOFM) artificial neural network which finds similar features in different resolution representations of the image. The patterns are mapped onto a two-dimensional array of formal neurons forming a code book similar to vector quantization (VQ) coding. We use the (SOFM) ordering properties by searching for mapping not only to the best feature match neuron but also to its neighbors in the network. In this paper we describe the ITT-SOFM algorithm and its software implementation with application to image coding of still gray images. computer simulations show compression results comparable to or better than state-of-the-art VQ coders, and computational complexity better than most of the well known clustering algorithms.

关键词： image coding

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neural network for improving the performance of nearest neighbor classifiers

Neural network for improving the performance of nearest neig...

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neural and stochastic methods in image and signal processing

作者： Yan, Hong Univ. of Sydney Sydney New South Wales Australia

ISBN: (纸本)0819409391

In a nearest neighbor classifier, an input sample is assigned to the class of the nearest prototype. The decision rule is simple and robust. However, it is computationally expensive in terms of memory space and computer time to implement a nearest neighbor classifier if each training sample is stored as a prototype and used to compare with every testing sample. The performance of the classifier is degraded if only a small number of training samples are used as prototypes. An algorithm is presented in this paper for modifying the prototypes so that the classification rate can be increased. This algorithm makes use of a two-layer perceptron with one second order input. The perceptron is trained and mapped back to a new nearest neighbor classifier. It is shown that the new classifier with only a small number of prototypes can even perform better than the classifier that uses all training samples as prototypes.

关键词： neural networks

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stochastic and neural methods in signal processing, image processing, and computer vision

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

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stochastic and neural methods in signal processing, image processing, and computer vision 1991

The proceedings contain 46 papers. The topic discussed include: considering multiple-surface hypotheses in a Bayesian hierarchy;example of a bayes network of relations among visual features;recursive computation of a wire-frame representation of a scene from dynamic stereo using belief functions;hierarchical Dempster-Shafer evidential reasoning for image interpretation;standard-cell design architecture options below 5nm node: the ultimate scaling of FINFET and nanosheet;and small target detection for search and rescue operations using distributed deep learning and synthetic data generation.

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TIMBRE DISCRIMINATION OF signalS WITH IDENTICAL PITCH USING neural NETWORKS

TIMBRE DISCRIMINATION OF SIGNALS WITH IDENTICAL PITCH USING ...

引用

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

作者： SAYEGH, S POMALAZARAEZ, C TEPPER, E BEER, B Purdue Univ. (United States)

Pitch recognition and timbre discrimination for a string instrument is investigated using artificial neural networks. Pitch recognition, the easier task, is realized with a linear classifier while timbre discriminatio... 详细信息

ISBN: (纸本)081940697X

关键词： neural networks signal processing image processing signal detection Artificial neural networks computer vision technology Linear filtering Machine vision Multilayers stochastic processes

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UTILIZING THE CENTRAL-LIMIT-THEOREM FOR PARALLEL MULTIPLE-SCALE image-processing WITH neural ARCHITECTURES

UTILIZING THE CENTRAL-LIMIT-THEOREM FOR PARALLEL MULTIPLE-SC...

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

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

ISBN: (纸本)081940697X

A set of neural lattices that are based on the central limit theorem is described. Each of the described lattices, generates in parallel a set of multiple scale Gaussian smoothing of their input arrays. The recursive smoothing principle of the lattices can be extended to any dimension. In addition, the lattices can generate in real time a variety of multiple scale operators such as Canny's edge detectors, Laplacians of Gaussians, and multidimensional sine, cosine, Fourier, and Gabor transforms.

关键词： Smoothing image processing Convolution 3D image processing Transform theory signal processing computer vision technology Machine vision neural networks stochastic processes

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SOME ANALYTICAL AND STATISTICAL PROPERTIES OF FISHER INFORMATION

SOME ANALYTICAL AND STATISTICAL PROPERTIES OF FISHER INFORMA...

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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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GENERALIZED NEOCOGNITRON MODEL FOR FACIAL RECOGNITION

GENERALIZED NEOCOGNITRON MODEL FOR FACIAL RECOGNITION

引用

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

作者： CHEN, S HONG, Y Univ. of North Carolina/Charlotte (United States) Dongguk Univ. (South Korea)

ISBN: (纸本)081940697X

The Fukushima's neocognitron model is generalized to a parallel neocognitron architecture which is applied for gray-scale facial recognition. Experiments show that the system can recognize human faces after learning. Results using the single neocognitron model for facial recognition or other gray-scale image recognition problems are not satisfactory.

关键词： Facial recognition systems computer vision technology image processing Machine vision Pattern recognition signal processing stochastic processes neural networks Visual process modeling Analog electronics

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