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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 131 条记录，以下是91-100 订阅

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ARTIFICIAL SCENES AND SIMULATED IMAGING

ARTIFICIAL SCENES AND SIMULATED IMAGING

引用

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

作者： REICHENBACH, SE ALTERGARTENBERG, R PARK, SK RAHMAN, ZU Univ. of Nebraska/Lincoln (United States) College of William and Mary (United States) Old Dominion Univ. (United States) Science and Technology Corp. (United States)

ISBN: (纸本)081940697X

A software simulation environment for controlled image processing research is described. The simulation is based on a comprehensive model of the end-to-end imaging process that accounts for statistical characteristics of the scene, image formation, sampling, noise, and display reconstruction. The simulation uses a stochastic process to generate super-resolution digital scenes with variable spatial structure and detail. The simulation of the imaging process accounts for the important components of digital imaging systems, including the transformation from continuous to discrete during acquisition and from discrete to continuous during display. This model is appropriate for a variety of problems that involve image acquisition and display including system design, image restoration, enhancement, compression, and edge detection. By using a model-based simulation, research can be conducted with greater precision, flexibility, and portability than is possible using physical systems and experiments can be replicated on any general-purpose computer.

关键词： image processing Digital imaging Imaging systems image acquisition stochastic processes computer simulations Digital image processing signal processing Point spread functions Machine vision

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COLOR SPACE ANALYSIS OF ROAD DETECTION ALGORITHMS

COLOR SPACE ANALYSIS OF ROAD DETECTION ALGORITHMS

引用

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

作者： CRISMAN, JD Northeastern Univ. (United States)

ISBN: (纸本)081940697X

Color space analysis of classification-based segmentation algorithms provides insights into the capabilities of the color vision system. In standard pattern recognition theory, classification algorithms are categorized by their discriminant functions in feature space. This analysis is applied to color classification methods used in road detection systems. There have been many systems that use classification techniques. By examining these road detection system in color space, a relationship can be seen between their color model representation and their capabilities and limitations.

关键词： Roads Classification systems 3D modeling 3D image processing RGB color model Systems modeling computer vision technology Detection and tracking algorithms image classification Machine vision

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SHAPE-DISCRIMINATION USING INVARIANT FOURIER REPRESENTATION AND A neural NETWORK CLASSIFIER

SHAPE-DISCRIMINATION USING INVARIANT FOURIER REPRESENTATION ...

引用

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

作者： WU, HH SCHOWENGERDT, RA Univ. of Arizona (United States)

ISBN: (纸本)081940697X

A neural network approach for classification of images represented by translation, scale, and rotation invariant features is presented. The invariant features are the Fourier descriptors (FDs) derived from the boundary (shape) of the object. The network is a multilayer perceptron (MLP) classifier with one hidden layer and back propagation training (MLP-BP). Performance of the MLP algorithm is compared to optimal curve matching (OCM) for the recognition of mechanical tools. The test data were 14 objects with eight images per object, each image having significant differences in scaling, translation, and rotation. Only 10 harmonics of the 1024 FD coefficients were used as the input vector. The neural network approach proved to be more stable and faster than the optimal curve matching algorithm in classifying the objects after the training phase. The simple calculations needed for the Fourier descriptors and the small number of coefficients needed to represent the boundary result in an efficient system, excluding training, which can be done off-line. Results are shown comparing the classification accuracy of the OCM method with the MLP-BP algorithm using different size training sets. The method can be extended to any patterns that can be discriminated by shape information.

关键词： neural networks image processing computer vision technology Machine vision stochastic processes signal processing image classification Detection and tracking algorithms Error analysis Statistical analysis

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FRACTIONAL BROWNIAN-MOTION AND ITS FRACTAL DIMENSION ESTIMATION

FRACTIONAL BROWNIAN-MOTION AND ITS FRACTAL DIMENSION ESTIMAT...

引用

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

作者： ZHANG, P MARTINEZ, A BARAD, H Tulane Univ. (United States)

ISBN: (纸本)081940697X

A mathematical model of stochastic processes -- fractional Brownian motion -- is addressed. The power-law behaviors of FBM increments are studied in detail for moments, correlation functions, and power spectra. A moment method is proposed to do model testing of fractional Brownian motion. The results of FBM model testing of six simulators show that the covariance matrix transforming algorithm can provide samples with very good approximation of self-affinity. The self-affinity of the FBM samples generated by Fourier transform filtering is not very obvious. The statistical properties of fractal dimension estimation methods are analyzed. The simulation results show that the variance method provides good performance when only estimates of variances with small time lags are used in the least-squares estimation. For the power spectrum method, the bias is not ignorable because of the aliasing and the window effect.

关键词： Fractal analysis Error analysis Fourier transforms stochastic processes image processing signal processing Statistical analysis computer vision technology Machine vision Motion models

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SIMULATED ANNEALING image-RECONSTRUCTION FOR AN X-RAY CODED SOURCE TOMOGRAPH

SIMULATED ANNEALING IMAGE-RECONSTRUCTION FOR AN X-RAY CODED ...

引用

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

作者： ELALAOUI, M MAGNIN, IE AMIEL, M INSA (France)

ISBN: (纸本)081940697X

The reconstruction of a 3-D object from its 2-D coded radiograph is considered. The set up is a restricted view angle tomographic system. The physical acquisition conditions are simulated. A 3-D phantom is reconstructed using the simulated annealing (SA) algorithm. Three initial solutions are envisaged: zero volume; a solution obtained by SA with T equals 0; and an algebraic reconstruction provided by a specific tomosynthesis algorithm. The quality of the reconstructions provided by the initial solutions is enhanced by using the algorithm. The root mean square distance between the coded projection of the actual object and the coded projection of the reconstructed object is minimized. The advantage of starting from a given initial solution is to increase the convergence speed and thus to minimize computation time. A computer simulation is performed in which a 9 source point distribution irradiates a 3-D object (64 X 64 X 64 voxels thick). The initial (first step) and final (second step) reconstructions of the objects are presented. The speed of convergence and the quality of the results are discussed.

关键词： Reconstruction algorithms Algorithms 3D image processing computer vision technology image processing Machine vision signal processing stochastic processes image restoration X-ray sources

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computer-GENERATED CORRELATED NOISE imageS FOR VARIOUS STATISTICAL DISTRIBUTIONS

COMPUTER-GENERATED CORRELATED NOISE IMAGES FOR VARIOUS STATI...

引用

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

作者： WENAAS, H WEEKS, AR MYLER, HR Martin Marietta Corp. (United States) Univ. of Central Florida (United States)

ISBN: (纸本)081940697X

The evaluation of image processing algorithms generally assumes images that are degraded by known statistical noise. The type of noise distributions that are needed depend on the nature of the application. The noise distributions that are commonly used are the Gaussian, negative exponential, and uniform distributions. Typically, these computer-generated noise images are spatially uncorrelated. It is the purpose of this paper to present computer-generated two- dimensional correlated and uncorrelated noise images that can be readily used in the evaluation of various image processing algorithms. Several statistical distributions including the negative exponential, the Rayleigh, and the K-distribution are generated from Gaussian statistical noise and are presented. For the generation of correlated noise images, the correlation function is defined by either describing the correlation function directly or by specifying the power spectral density function (PSD) using the Weiner-Kinchine theorem. These computer synthesized images are then compared against the expected theoretical results. Additionally, the autocorrelation function for the computer-generated noise images are computed and compared against the specified autocorrelation function. Also, included in the theoretical analysis is the effect of quantization, and finite pixel intensity, i.e., 0 - 255. Finally, several uncorrelated and correlated noise images are presented.

关键词： image processing Filtering (signal processing) computer vision technology Machine vision signal processing stochastic processes image filtering Gaussian filters Digital filtering Interference (communication)

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LINEAR FEATURE SNR ENHANCEMENT IN RADON-TRANSFORM SPACE

LINEAR FEATURE SNR ENHANCEMENT IN RADON-TRANSFORM SPACE

引用

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

作者： MECKLEY, JR GE Advanced Technology Labs. (United States)

ISBN: (纸本)081940697X

Many image features of interest are either linear in nature or are composed of piecewise linear segments. When the initial imaging process does not produce a signal-to-noise ratio sufficient for detection, a predetection filter is required to enhance the feature SNR. This filter must be invariant to feature position, orientation, and size in order to produce the highest processing gain with minimum distortion. The Fourier transform of the radon transform of linear features is shown to be invariant with respect to position and orientation, while varying slowly with respect to feature size. This permits optimum filtering for SNR enhancement. After filtering the radon transform, the image is reconstructed through a backprojection algorithm. Detection and segmentation of the linear features is significantly enhanced in the filtered image.

关键词： signal to noise ratio Radon transform image filtering Filtering (signal processing) image processing signal processing Fourier transforms Linear filtering image enhancement image segmentation

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HIERARCHICAL DEMPSTER-SHAFER EVIDENTIAL REASONING FOR image INTERPRETATION

HIERARCHICAL DEMPSTER-SHAFER EVIDENTIAL REASONING FOR IMAGE ...

引用

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

作者： ANDRESS, KM Siemens Corporate Research (United States)

ISBN: (纸本)081940697X

A hierarchical evidence accumulation scheme developed for use in blackboard systems is described. This scheme, based on the Dempster-Shafer formalism, uses a computationally efficient variation of Dempster's rule of combination enabling the system to deal with the overwhelming amount of information present in image data. This variation of Dempster's rule allows the reasoning process to be embedded into the abstraction hierarchy by allowing for the propagation of belief values between elements at different levels of abstraction. The evidence accumulation scheme described here was originally designed to be embedded in PSEIKI, a blackboard system for expectation-driven interpretation of image data. PSEIKI performs expectation-driven processing by matching image-elements, such as edges and regions, with model-elements from a supplied expected scene. PSEIKI builds abstraction hierarchies in image data using cues taken from the supplied abstractions in the expected scene. Hypothesized abstractions in the image data are geometrically compared with the known abstractions in the expected scene; the metrics used for these comparisons translate into belief values. The evidence accumulation system is described in detail and a few representative metrics also are presented.

关键词： Data modeling image processing computer vision technology Machine vision Computing systems signal processing stochastic processes Chemical elements Composites Visual process modeling

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RECURSIVE COMPUTATION OF A WIRE-FRAME REPRESENTATION OF A SCENE FROM DYNAMIC STEREO USING BELIEF FUNCTIONS

RECURSIVE COMPUTATION OF A WIRE-FRAME REPRESENTATION OF A SC...

引用

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

作者： TIRUMALAI, AP SCHUNCK, BG JAIN, RC Univ. of Michigan (United States)

ISBN: (纸本)081940697X

This paper presents a stereo algorithm to recursively compute a boundary-level structural description of a scene, from a sequence of stereo images. The majority of existing stereo algorithms deal with individual points as the basic primitive to match between two or more images. While this keeps the implementation simple, the output description, which is a depth/disparity map, is represented as a composition of individual points. This is often undesirable as no semblance of the underlying structure of the scene is explicitly represented. A stereo matching algorithm is presented, based on connected line segments as the basic match primitive, which yields a description composed primarily of boundaries of objects in the scene. A description of this nature is very useful for obstacle avoidance and path planning for mobile robots. The stereo matching algorithm is integrated into a dynamic stereo vision system to compute and incrementally refine such a structural description recursively, using belief functions. The stereo camera motion between two viewpoints, which is necessary to register the two views, is recovered as part of the stereo computations. The approach is illustrated with a real dynamic stereo sequence acquired from a mobile robot.

关键词： image segmentation Cameras image processing signal processing computer vision technology Machine vision stochastic processes 3D image processing Mobile robots Motion estimation

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FREQUENCY BASED PATTERN-RECOGNITION USING neural NETWORKS

FREQUENCY BASED PATTERN-RECOGNITION USING NEURAL NETWORKS

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

作者： LU, SW Hyper-L Inc. (United States)

ISBN: (纸本)081940697X

A pattern recognition algorithm is proposed, in frequency domain, using a backpropagation neural network. The algorithm extracts distinct frequency features from reference patterns and compares them with the corresponding features of an unknown pattern. The feature sets are learned and recognized through backpropagation neural networks. Multiple neural networks are used to form a classification network. Since the feature set extracted is significantly smaller than the pattern image, the neural network is fast and accurate. Preliminary results indicates that certain features in frequency domain remain consistent for images of the same pattern and it is possible to extract these features for pattern recognition. Experimental results have also indicated that these features can be used to distinguish one pattern from another accurately. The system presented herein exhibits the advantages over previous systems of increasing recognition accuracy, i.e., lower false identification rate, increasing recognition speed, and decreasing data storage space. A method to dynamically identify frequency features in any set of reference patterns and to classify an unknown feature set using neural network is described in detail.

关键词： neural networks Databases image processing Feature extraction Pattern recognition Detection and tracking algorithms Neurons signal processing stochastic processes Machine vision

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