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CONF ON NEURAL AND stochastic methods IN image AND SIGNAL processing 2

作者： BALARAM, J Jet Propulsion Lab. (United States)

ISBN: (纸本)0819412813

image differencing based inspection allows the comparison of a prior reference image with a subsequent inspection image to detect changes that can be attributed to flaw induced damage between inspection periods. In our application, the inspection system is required to find new flaws to orbiting space structures that might arise as a result of micro-meteorites, space debris, effects of atomic oxygen damage etc. Direct differencing of these `before' and `after' signals for the flaw detection problem is complicated by changes that are not the result of flaws. These benign changes result from the image noise, mis-registration effects induced by non- repeatability in the imaging viewpoint at each inspection period, and variability of ambient illumination resulting from change of solar angle induced by orbital motion. In this paper we discuss the methods by which some of these effects are handled and suggest a modeling framework using statistical mechanics techniques.

关键词： Inspection Mechanics Cameras Oxygen image registration statistical modeling Ultraviolet radiation Fiber optic illuminators Wind energy Signal detection

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BAYESIAN PART TOLERANCING WITH MEASUREMENT UNCERTAINTY 2

BAYESIAN PART TOLERANCING WITH MEASUREMENT UNCERTAINTY

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CONF ON NEURAL AND stochastic methods IN image AND SIGNAL processing 2

作者： NOBLE, A MUNDY, J GE Corporate Research and Development Ctr. (United Kingdom) GE Corporate Research and Development Ctr. (United States)

ISBN: (纸本)0819412813

Gibbs sampling, and other stochastic simulation methods, have recently received considerable attention in Bayesian statistics. Significant progress has been made in the areas of developing techniques for sampling from non-conjugate distributions, and analyzing theoretical and practical aspects relating to convergence. One of the powers of Gibbs sampling is the way it can simplify the expression of data models by replacing the evaluation of the integrals needed to compute the relevant posterior quantities by sampling from multidimensional distributions. This has opened up the way to solve complex Bayesian models that are not analytically tractable. In this paper we show how to separate variability in model parameters from variability due to the model extraction process by fitting hierarchial models to image sequences using Gibbs sampling. First, we review some of the recent developments in Gibbs sampling. Then we describe some of our experimental work using Gibbs sampling to extract geometric parameter distributions from industrial images.

关键词： statistical analysis Error analysis Tolerancing Manufacturing statistical modeling image processing Process modeling image segmentation Visual process modeling Data modeling

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ROBUST FRACTAL CHARACTERIZATION OF 1-D AND 2-D SIGNALS 2

ROBUST FRACTAL CHARACTERIZATION OF 1-D AND 2-D SIGNALS

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CONF ON NEURAL AND stochastic methods IN image AND SIGNAL processing 2

作者： AVADHANAM, N MITRA, S Texas Tech Univ. (United States)

ISBN: (纸本)0819412813

Fractal characterization of signals is well suited in analysis of some time series data and in classification of natural shapes and textures. A maximum likelihood estimator is used to measure the parameter H which is directly related to the fractal dimension. The robustness of the estimator and the performance of the method are demonstrated on datasets generated using a variety of techniques. Finally the characterization is used in segmentation of composite images of natural textures.

关键词： Fractal analysis image segmentation Composites Error analysis statistical analysis image classification Polymers Edge detection Computer vision technology Machine vision

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SPATIOTEMPORAL PATTERN-RECOGNITION USING HIDDEN MARKOV-MODELS 2

SPATIOTEMPORAL PATTERN-RECOGNITION USING HIDDEN MARKOV-MODEL...

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CONF ON NEURAL AND stochastic methods IN image AND SIGNAL processing 2

作者： FIELDING, KH RUCK, DW ROGERS, SK WELSH, BM OXLEY, ME Air Force Institute of Technology (United States)

ISBN: (纸本)0819412813

A spatio-temporal method for identifying objects contained in an image sequence is presented. The Hidden Markov Model (HMM) technique is used as the classification algorithm, making classification decisions based on a spatio-temporal sequence of observed object features. A five class problem is considered. Classification accuracies of 100% and 99.7% are obtained for sequences of images generated over two separate regions of viewing positions. HMMs trained on image sequences of the objects moving in opposite directions showed a 98.1% successful classification rate by class and direction of movement. The HMM technique proved robust to image corruption with additive correlated noise and had a higher accuracy than a single look nearest neighbor method. A real image sequence of one of the objects used was successfully recognized with the HMMs trained on synthetic data. This study shows the temporal changes that observed feature vectors undergo due to object motion hold information that can yield superior classification accuracy when compared to single frame techniques.

关键词： Signal to noise ratio Optical spheres Pattern recognition Error analysis image classification image processing Motion models 3D modeling statistical analysis statistical modeling

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EXPLICIT NOISE HYPOTHESES IN SPEECH RECOGNITION 2

EXPLICIT NOISE HYPOTHESES IN SPEECH RECOGNITION

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CONF ON NEURAL AND stochastic methods IN image AND SIGNAL processing 2

作者： FOX, R JOSEPHSON, J Univ. of Texas Pan American (United States) The Ohio State Univ. (United States)

ISBN: (纸本)0819412813

Noise is typically present in the input signal for perception problems. Noise arises in speech recognition due to both background sounds, and unintentional derivations from the intended utterance on the part of the speaker. The task of speech recognition is to correctly identify the words (or meaning) carried by the speech signal. Thus the speech recognizer must be able to successfully handle noise. We describe here a method of explicitly identifying and labeling noise elements in a speech signal. NOISE hypotheses are generated, and considered for acceptance, as part of an abductive inference strategy for speech processing. An abductive problem solver is able to treat noise within a unified inferential framework, treating noise hypotheses similarly to other hypotheses, weighing the explanatory alternatives in a context-sensitive manner, and with no need to resort to indirect methods to achieve noise tolerance.

关键词： Speech recognition Interference (communication) Signal processing Composites Tongue Laser induced plasma spectroscopy Acoustics Data modeling Neural networks statistical modeling

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MULTISCALE REPRESENTATIONS OF MARKOV RANDOM-FIELDS

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IEEE TRANSACTIONS ON SIGNAL processing 1993年第12期41卷 3377-3396页

作者： LUETTGEN, MR KARL, WC WILLSKY, AS TENNEY, RR MIT INFORMAT & DECIS SYST LABCAMBRIDGEMA 02139 MIT DEPT ELECT ENGN & COMP SCICAMBRIDGEMA 02139

Recently, a framework for multiscale stochastic modeling was introduced based on coarse-to-fine scale-recursive dynamics defined on trees. This model class has some attractive characteristics which lead to extremely efficient, statistically optimal signal and image processing algorithms. In this paper, we show that this model class is also quite rich. In particular, we describe how 1-D Markov processes and 2-D Markov random fields (MRF's) can be represented within this framework. The recursive structure of 1-D Markov processes makes them simple to analyze, and generally leads to computationally efficient algorithms for statistical inference. On the other hand, 2-D MRF's are well known to be very difficult to analyze due to their noncausal structure, and thus their use typically leads to computationally intensive algorithms for smoothing and parameter identification. In contrast, our multiscale representations are based on scale-recursive models and thus lead naturally to scale-recursive algorithms, which can be substantially more efficient computationally than those associated with MRF models. In 1-D, the multiscale representation is a generalization of the midpoint deflection construction of Brownian motion. The representation of 2-D MRF's is based on a further generalization to a ''midline'' deflection construction. The exact representations of 2-D MRF's are used to motivate a class of multiscale approximate MRF models based on one-dimensional wavelet transforms. We demonstrate the use of these latter models in the context of texture representation and, in particular, we show how they can be used as approximations for or alternatives to well-known MRF texture models.

关键词： Markov random fields Markov processes Algorithm design and analysis Inference algorithms Context modeling stochastic processes Signal processing image processing Smoothing methods Parameter estimation

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Multiresolution statistical methods in image analysis

Multiresolution statistical methods in image analysis

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Intelligent Robots and Computer Vision XI: Biological, Neural Net, and 3-D methods

作者： Luettgen, Mark R. Karl, William C. Willsky, Alan S. Tenney, Robert R. Massachusetts Inst. of Technology Cambridge MA USA

ISBN: (纸本)0819410276

In this paper, we discuss a statistical framework for multiscale signal and image processing based on a class of multiresolution stochastic models, which can be used to represent spatial random processes at a range of scales. The model class is quite rich, and in fact includes the class of Markov random fields. In addition, the models have a scale recursive structure which naturally leads to efficient, scale recursive algorithms for smoothing and likelihood calculation. We discuss an application of the framework to the problem of computing optical flow in image sequence, and demonstrate computational savings on the order of one to two orders of magnitude over standard algorithms.

关键词： image analysis

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Cluster approximations for statistical image processing

Cluster approximations for statistical image processing

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Neural and stochastic methods in image and Signal processing II

作者： Wu, Chi-hsin Doerschuk, Peter C.

ISBN: (纸本)0819412813

A disadvantage of using discrete-state Markov random field models of images is that optimal estimators for reconstruction problems require excessive and typically random amounts of computation. In one approach the key task is the computation of the conditional mean of the field given the data or equivalently the unconditional mean of the a posteriori field. In this paper we describe a hierarchy of deterministic parallelizable methods for such computations.

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methods for numerical integration of high-dimensional posterior densities with application to statistical image models

Methods for numerical integration of high-dimensional poster...

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Neural and stochastic methods in image and Signal processing II

作者： LaValle, Steven M. Moroney, Kenneth J. Hutchinson, Seth A.

ISBN: (纸本)0819412813

Numerical computation with Bayesian posterior densities has recently received much attention both in the statistics and computer vision communities. This paper explores the computation of marginal distributions for models that have been widely considered in computer vision. These computations can be used to assess homogeneity for segmentation, or can be used for model selection. In particular, we discuss computation methods that apply to a Markov random field formation, implicit polynomial surface models, and parametric polynomial surface models, and present some demonstrative experiments.

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Topology and parameters estimation in Markov random field modelling 2

Topology and parameters estimation in Markov random field mo...

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Neural and stochastic methods in image and Signal processing II 1993

作者： Descombes, Xavier Preteux, Françoise Département IMAGE TELECOM-PARIS 46 rue Barrault Paris75013 France

Within the framework of pattern recognition via Markov random field modelling, we propose three methods for estimating the topological and statistical parameters characterizing the model, namely clique orders, anisotropy indices, weighting coefficient between cliques with various orders, coefficients of polynomial potential functions and temperature. The developed approaches successively exploit local information associated with conditional probability distributions, a similarity criterion expressed as a distance in variations between appropriate probability distributions, standard least-square estimation and renormalization theory. Extensive experiments performed on a variety of synthetic images have established the relevance and accuracy of the proposed method. Its performances are further demonstrated within the framework of urban areas segmentation in SPOT images. © 1993 SPIE. All rights reserved.

关键词： Parameter estimation

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