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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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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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FREQUENCY BASED PATTERN-RECOGNITION USING neural NETWORKS

FREQUENCY BASED PATTERN-RECOGNITION USING NEURAL NETWORKS

引用

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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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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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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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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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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stochastic FIELD-BASED OBJECT RECOGNITION IN COMPUTER VISION

STOCHASTIC FIELD-BASED OBJECT RECOGNITION IN COMPUTER VISION

引用

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

作者： ZHU, DP BEEX, AAL CONNERS, R Virginia Polytechnic Institute and State Univ. (United States)

ISBN: (纸本)081940697X

This study explores the application of a stochastic texture modeling method toward a machine vision system for log inspection in the forest products industry. This machine vision system uses computerized tomography (CT) imaging to locate and identify internal defects in hardwood logs. To apply CT to these industrial vision problems requires efficient and robust image analysis methods. The paper addresses one aspect of the problem of creating such a computer vision system, i.e., the issue of statistical image texture modeling for wood defect recognition using a stochastic field-based approach. In particular, it describes a parametric model-based method for studying the spatial stochastic processes -- wood grain textures, with each grain texture being modeled by a parametric random field model. A robust algorithm for parameter estimation is applied to obtain model parameters for individual defects occurring inside a log. By making use of the estimated model features, a simple minimum distance classifier is constructed to classify an unknown defect into one of the prototypical defects. Experimental results of the proposed method with CT images from red oaks are given to show the efficacy of the proposed approach.

关键词： Visual process modeling Machine vision stochastic processes Computing systems Computer vision technology image processing Computed tomography image segmentation Autoregressive models signal processing

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image SEGMENTATION WITH GENETIC ALGORITHMS - A FORMULATION AND IMPLEMENTATION

IMAGE SEGMENTATION WITH GENETIC ALGORITHMS - A FORMULATION A...

引用

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

作者： SEETHARAMAN, G NARASIMHAN, A SATHE, A STORC, L Univ. of Southwestern Louisiana (United States) Cogent Research Inc. (United States)

ISBN: (纸本)081940697X

image segmentation is an important step in any computer vision system. Segmentation refers to the partitioning of the image plane into several regions, such that each region corresponds to a logical entity present in the scene. The problem is inherently NP, and the theory on the existence and uniqueness of the ideal segmentation is not yet established. Several methods have been proposed in literature for image segmentation. With the exception of the state-space approach to segmentation, other methods lack generality. The state-space approach, however, amounts to searching for the solution in a large search space of 22n(2) possibilities for a n X n image. In this paper, a classic approach based on state-space techniques for segmentation due to Brice and Fennema is reformulated using genetic algorithms. The state space representation of a partially segmented image lends itself to binary strings, in which the dominant substrings are easily explained in terms of chromosomes. Also the operations such as crossover and mutations are easily abstracted. In particular, when multiple images are segmented from an image sequence, fusion of constraints from one to the other becomes clear under this formulation.

关键词： image segmentation Genetic algorithms image fusion Computer vision technology Machine vision image processing Computing systems signal processing stochastic processes Binary data

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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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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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