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

共 540 条记录，以下是511-520 订阅

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RECOVERING ABSOLUTE DEPTH AND MOTION OF MULTIPLE OBJECTS FROM INTENSITY imageS

RECOVERING ABSOLUTE DEPTH AND MOTION OF MULTIPLE OBJECTS FRO...

引用

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

作者： JIANG, F SCHUNCK, BG Univ. of Michigan (United States)

ISBN: (纸本)081940697X

This paper reports an algorithm that recovers absolute depth and motion of multiple objects from intensity images. It has been shown that absolute depth of multiple objects can be recovered from relative normal flows. With normal flows and depth estimated from images, motion recovery becomes a linear regression problem. As the depth estimates can be very noisy, the method of least-median of squares (LMS) is used to robustly recover the object motion. To decompose image points into groups that correspond to independently moving objects, strong-edge points at which normal flows are estimated are grouped into segments. The segments are then grouped into rigidly moving objects if they can be interpreted as such. This algorithm is designed for the case of general motion. However, experiments suggest that rotation makes the estimation of quality normal flows difficult and brings large errors into depth and motion recovery. The algorithm is tested on real images of translating objects.

关键词： image segmentation Cameras Motion estimation image processing algorithms and systems image processing Error analysis Imaging systems signal processing stochastic processes Machine vision

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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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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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ROBUST REGULARIZED image-RESTORATION

ROBUST REGULARIZED IMAGE-RESTORATION

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

作者： KWON, TM ZERVAKIS, ME Univ. of Minnesota/Duluth (United States)

ISBN: (纸本)081940697X

Many image processing applications involve long-tailed noise processes, which introduce outliers in the gray-level distribution of the image. The performance of conventional restoration algorithms is highly degraded by such noise processes. A novel restoration approach is introduced, which combines the properties of regularized and robust estimation schemes. Most prominent regularized approaches attempt to compensate for the ill-posedness of the pseudo-inverse solution. Regularization is achieved by constraining the least squares solution in terms of a smoothing criterion. The optimization approach introduced in this paper further modifies the regularized criterion according to the notion of M-estimation. Thus, an influence function is employed in restraining the contribution of large estimate-deviations in the optimization criterion. The modified criterion provides nonlinear estimates, which do not suffer from artifacts due to the presence of long-tailed noise. The computation of the robust regularized estimate is based on the simple structure of a steepest-descent iterative procedure. One of the most important factors associated with the concept of regularization is the regularization parameter. Adaptive schemes concerning the selection of this parameter at every iteration step are introduced. The convergence properties of the robust and the adaptive algorithms introduced are extensively studied. The capabilities of the robust regularized algorithms are demonstrated through restoration examples.

关键词： image processing image restoration signal processing stochastic processes Computer vision technology Machine vision image quality Reconstruction algorithms Algorithm development Statistical analysis

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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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BAYESIAN signal RECONSTRUCTION FROM FOURIER-TRANSFORM MAGNITUDE AND X-RAY CRYSTALLOGRAPHY

BAYESIAN SIGNAL RECONSTRUCTION FROM FOURIER-TRANSFORM MAGNIT...

引用

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

作者： DOERSCHUK, PC Purdue Univ. (United States)

ISBN: (纸本)081940697X

A signal reconstruction problem motivated by x-ray crystallography is solved using a Bayesian statistical approach. A Markov random field is used to describe the a priori information concerning the 0 - 1 signal. The data are inaccurate measurements of the magnitudes of the Fourier coefficients of the signal. The solution exploits the parallel between Bayesian statistics and statistical mechanics and uses the spherical model and asymptotic small noise approximations.

关键词： Crystallography X-rays Fourier transforms Spherical lenses Chemical species Magnetorheological finishing Physics signal processing Molecules stochastic processes

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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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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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MEAN-FIELD THEORY FOR GRAYSCALE TEXTURE SYNTHESIS USING GIBBS RANDOM-FIELDS

MEAN-FIELD THEORY FOR GRAYSCALE TEXTURE SYNTHESIS USING GIBB...

引用

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

作者： ELFADEL, IM YUILLE, AL Massachusetts Institute of Technology (United States) Harvard Univ. (United States)

ISBN: (纸本)081940697X

This paper shows how methods developed in the context of statistical physics can be used to analyze gray-scale texture synthesis procedures based on the probabilistic paradigm of Gibbs random fields (GRF). In particular, using the mean-field equations of the texture GRF, the existence of a bifurcation point indicating the presence of a phase transition in the textural pattern is shown. Using simulations, it is shown that a number of interesting phenomena occur at the bifurcation temperature like a sudden decrease in energy, sharp peaks in similarity measures between textural patterns, and sudden saturation of the mean-field variables. For texture synthesis, it is sufficient to simulate the mean-field equations near the bifurcation temperature.

关键词： Binary data Computer vision technology Machine vision image processing signal processing stochastic processes Annealing Visual process modeling Computer simulations Physics

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LEARNING SPATIALLY COHERENT PROPERTIES OF THE VISUAL WORLD IN CONNECTIONIST NETWORKS

LEARNING SPATIALLY COHERENT PROPERTIES OF THE VISUAL WORLD I...

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

作者： BECKER, S HINTON, GE Univ. of Toronto (Canada)

ISBN: (纸本)081940697X

In the unsupervised learning paradigm, a network of neuron-like units is presented with an ensemble of input patterns from a structured environment, such as the visual world, and learns to represent the regularities in that input. The major goal in developing unsupervised learning algorithms is to find objective functions that characterize the quality of the network's representation without explicitly specifying the desired outputs of any of the units. The sort of objective functions considered cause a unit to become tuned to spatially coherent features of visual images (such as texture, depth, shading, and surface orientation), by learning to predict the outputs of other units which have spatially adjacent receptive fields. Simulations show that using an information-theoretic algorithm called IMAX, a network can be trained to represent depth by observing random dot stereograms of surfaces with continuously varying disparities. Once a layer of depth-tuned units has developed, subsequent layers are trained to perform surface interpolation of curved surfaces, by learning to predict the depth of one image region based on depth measurements in surrounding regions. An extension of the basic model allows a population of competing neurons to learn a distributed code for disparity, which naturally gives rise to a representation of discontinuities.

关键词： stochastic processes Visualization image processing Sensors Machine learning Computer vision technology Machine vision signal processing Algorithm development Stereo holograms

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