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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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Noncausal predictive image coding

Noncausal predictive image coding

引用

Conference on neural, Morphological, and stochastic methods in image and signal processing

作者： ZHOU, PF NEWBRIDGE NETWORKS CORP KANATAON K2K 2E6CANADA

ISBN: (纸本)0819419273

This paper presents an application of Markov field theory to image coding. First we use Markov random fields to model the correlation in the image intensity fields. We then propose a noncausal predictive image coding scheme in which the estimation of present pixel is based on both past and future neighboring pixels. A decoding algorithm is proposed to perfectly reconstruct the image from estimation residuals at the decoder. Open-loop and closed-loop quantizer structures are implemented for noncausal prediction and performances are compared with conventional DPCM predictive coding.

关键词： MARKOV RANDOM FIELD NONCAUSAL image CODING

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VLSI implementable parallel stochastic learning algorithm

VLSI implementable parallel stochastic learning algorithm

引用

Conference on neural, Morphological, and stochastic methods in image and signal processing

作者： RUIZ, LV PANDYA, AS FLORIDA ATLANTIC UNIV DEPT COMP SCI & ENGNBOCA RATONFL 33431

ISBN: (纸本)0819419273

A VLSI implementable, massively parallel, digital, stochastic neural network architecture with on-chip learning is described which can be used to address video/image compression applications. Color information in images is usually encoded as a means to reduce the data needed to transmit high resolution video images over a lower bandwidth communication system. A neural net approach can be used for such a compression scheme since it can be trained to map a set of patterns from a k-dimensional space to a 1D space very easily. The training algorithm is implemented as a cross correlation between previously calculated weights and global errors. These simple calculations are performed by each processing unit with information available in their local memories, thus, no transfer of information between neurons is necessary. This feature allows for the synchronous updating of all the weights, which optimizes the inherent parallel nature of the neural network architecture, making the design an excellent candidate for VLSI implementation as an SIMD architecture. By incorporating the learning portion on-chip, an appreciable reduction in computing time is possible. The stochastic nature of the learning algorithm, and a simulated 'annealing' process, allows it to convergence to a global minimum. The architecture is synthesized from an HDL description using powerful design and analysis tools which allow for many performance trade- offs and fault coverage to be done by the compiler before the final design is sent for fabrication.

关键词： ARTIFICIAL neural NETWORK stochastic LEARNING ALGORITHM PARALLEL ARCHITECTURE HARDWARE IMPLEMENTATION

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Fast analog associative memory

Fast analog associative memory

引用

Conference on neural, Morphological, and stochastic methods in image and signal processing

作者： KINSER, JM ALABAMA A&M UNIV DEPT PHYSNORMALAL 35762

ISBN: (纸本)0819419273

Traditional neural networks such as backpropagation begin with a set of decision boundaries and optimize the network by moving the boundaries. The problem with this approach is a large number of iterations is required and the network can easily be stuck in a local minima. The algorithm presented here rapidly creates boundaries when necessary and destroys boundaries when they become obsolete. Optimization is achieved by a 'survival of the fittest' boundaries approach. Since the individual boundaries are not optimized the algorithm does not require iterations and trains the network very quickly. The algorithm is well suited for high- dimensional analog inputs and analog outputs.

关键词： ASSOCIATIVE MEMORY neural NETWORKS

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High-order neural networks for image recognition

High-order neural networks for image recognition

引用

Conference on neural, Morphological, and stochastic methods in image and signal processing

作者： UWECHUE, OA PANDYA, AS SZABO, P FLORIDA ATLANTIC UNIV DEPT COMP ENGNBOCA RATONFL 33431

ISBN: (纸本)0819419273

This paper discusses the application of high-order neural networks (HONNs) for image recognition and image enhancement of digitized images. A key property of neural networks is their ability to recognize invariances and extract essential parameters from complex high- dimensional data. The most significant advantage of the HONN over first-order networks is that invariances to geometric transformations can be incorporated into the network and need not be learned through iterative weight updates. A third-order HONN can be used to achieve translation, scale, and rotation invariant recognition with a significant reduction in training time over other neural net paradigms such as the multilayer perceptron. We have developed a model based on a third-order net that can be trained with various images. Simulation results show that the model is able to perform very well with images embedded in noise. It is also shown that this method outperforms the Hamming net. Our model has also been applied to another difficult and computationally-complex problem: human face recognition. We put forth arguments for the use of isodensity information in the recognition algorithm. A method of image recognition that fuses isodensity information and neural networks is described and its merits over other image recognition methods are expounded. It is shown that isodensity information coupled with the use of an 'adaptive threshold' strategy yields a system that is to a high degree unperturbed by image contrast noise. Simulation results for these applications are presented in the paper.

关键词： neural NETWORK HIGH-ORDER INVARIANCE image RECOGNITION ISODENSITY

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Valuation of image extrema using alternating filters by reconstruction

Valuation of image extrema using alternating filters by reco...

引用

Conference on neural, Morphological, and stochastic methods in image and signal processing

作者： VACHIER, C VINCENT, L ENSMP CTR MORPHOL MATHF-77305 FONTAINEBLEAUFRANCE

ISBN: (纸本)0819419273

image extrema are often used for locating the structures present in an image. Their extraction and their selection is a classic image segmentation preprocessing problem. One of the most powerful morphological tools for selecting significant extrema in a grayscale is to use their dynamics. However, a drawback of this technique is that minima and maxima (the dark and light structures they point out) are processed independently. We show in the paper that using the dynamics comes down to measuring the persistence of image minima (resp. maxima) when processing the image via a increasing (resp. decreasing) family of contrast filters. This principle can be generalized to any increasing family of morphological filters by reconstruction and leads to a general method for valuating image minima with respect to any criterion: size, shape, contrast. This proposition is still true for families of alternating filters whose main characteristic is to have a self dual behavior. In this paper we concentrate on this point. A symmetrical equivalent for the dynamics is defined and an efficient technique of computation is proposed. One of its key concept is a merging tree of extrema. The usefulness of this notion in image segmentation applications is also illustrated.

关键词： image ANALYSIS MATHEMATICAL MORPHOLOGY EXTREMA DYNAMICS ALTERNATING FILTERS GRAYSCALE RECONSTRUCTION SEGMENTATION

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Hierarchical statistical models for the fusion of multiresolution image data

Hierarchical statistical models for the fusion of multiresol...

引用

Conference on neural, Morphological, and stochastic methods in image and signal processing

作者： LAFERTE, JM HEITZ, F PEREZ, P FABRE, E INST RECH INFORMAT & SYST ALEATOIRES F-35042 RENNESFRANCE

ISBN: (纸本)0819419273

This paper presents a class of nonlinear hierarchical algorithms for the fusion of multiresolution image data in low-level vision. The approach combines nonlinear causal Markov models defined on hierarchical graph structures, with standard Bayesian estimation theory. Two random processes defined on simple hierarchical graphs (quadtrees or 'ternary graphs') are introduced to represent the multiresolution observations at hand and the hidden labels to be estimated. Two optimal algorithms (inspired from Viterbi's algorithm) are developed on the quadtree structure. The first one gives the exact solution for the MAP (maximum a posteriori) estimator. The second one gives, on the same structure, the global minimum of an energy function more relevant than the MAP criterion. These algorithms are noniterative, estimates being obtained within two passes on the graph structure. They are compared to an extension of the multiscale algorithm proposed by Bouman et al. which is adapted here to multiresolution data fusion.

关键词： FUSION MARKOV MODELS MULTIRESOLUTION processing BAYESIAN ESTIMATION VITERBI ALGORITHMS

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Mathematical morphology tools for 3-D image analysis of porous media

Mathematical morphology tools for 3-D image analysis of poro...

引用

Conference on neural, Morphological, and stochastic methods in image and signal processing

作者： DEMENDONCA, U NETO, B LOTUIO, RD UNIV FED PERNAMBUCO DEPT ELETRON & SISTEMASRECONHECIMENTO PADROES LABBR-50711970 RECIFEPEBRAZIL

ISBN: (纸本)0819419273

This paper presents mathematical morphology tools for 3D image analysis, namely, the geodesic granulometries and the neck histogram. The family of openings which constitutes the geodesic granulometries is parameterized by the radius of the digital disks utilized as structuring elements. We demonstrate the validity of the granulometry thus obtained. The resulting granulometric distributions are determined by the underlying metric associated with the digital disks. Next we propose an algorithm to compute the neck histogram, which is an analysis tool that gives statistical information concerning the occurrence of constrictions in the object studied. Finally, we demonstrate the applicaiton of the proposed analysis tools in the characterization of a 3D experimental sample designed as a model for a porous medium.

关键词： MATHEMATICAL MORPHOLOGY 3D image ANALYSIS GRANULOMETRY

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Texture analysis using genetic algorithms and partially ordered Markov models

Texture analysis using genetic algorithms and partially orde...

引用

Conference on neural, Morphological, and stochastic methods in image and signal processing

作者： DAVIDSON, J HUA, X ASHLOCK, D IOWA STATE UNIV SCI & TECHNOL DEPT ELECT & COMP ENGNIOWA CITYIA

ISBN: (纸本)0819419273

When using approaches for solving imaging problems such as maximum likelihood or a Bayesian decision rule, massive amounts of data are involved. In order to make the implementation on computers attainable and not overly CPU-intensive, approximations to optimal solutions are often chosen, or nonoptimal solutions sought. In this paper we present a novel solution to the problem of solving for a maximum a posteriori estimator that uses genetic algorithms to search the solution space and a new statistical model called partially ordered Markov models (POMMs). We apply the procedure to the problem of parameter fitting to stochastic models for texture. POMMs are a subclass of Markov random fields that have been shown to offer computational advantages over general Markov rnadom fields. POMMs are based on partial orderings of the lattice array. Among other properties, these models have an exact closed-form joint distribution. We show that POMMs can be used successfully for parameter fitting to texture data. A genetic algorithm is used for approximation of a solution of the maximum likelihood estimator. We also show simulated textures representing samples of the solutions found.

关键词： Genetic algorithms Data modeling stochastic processes Genetics Magnetorheological finishing image processing Statistical analysis Gallium image classification Statistical modeling

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Characterization of translation invariant elementary operators for gray-level morphology

Characterization of translation invariant elementary operato...

引用

Conference on neural, Morphological, and stochastic methods in image and signal processing

作者： BANON, GJF INST NACL PESQUISAS ESPACIAIS DIV PROCESSAMENTO IMAGENSBR-12201970 S JOSE CAMPOSSPBRAZIL

ISBN: (纸本)0819419273

The four classes of mathematical morphology elementary operators: dilations, erosions, anti- dilations, and anti-erosions have proved to be of fundamental importance of the decomposition/representation of any mapping between complete lattices. In this paper, we are concerned with the characterization of translation invariant window elementary operators (with window W) that transform a gray-level image with finite range K₁ into a gray-level image with possibly different finite range K₂. Three types of characterization are presented. In the first characterization, called 'characterization by confrontation' each elementary operator depends on a family of mappings from W to K₁, called structuring element. In the second characterization, called 'characterization by selection' each elementary operator depends on a family of mappings from W to K₂, called impulse response. Finally, in the third characterization, called 'characterization by decomposition' each elementary operator depends on a family of mappings for K₁ to K₂, called elementary look up tables.

关键词： MATHEMATICAL MORPHOLOGY image processing DILATION EROSION WINDOW OPERATOR TRANSLATION INVARIANT OPERATOR CHARACTERIZATION HEIJMANS OPERATOR

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Adaptive Markovian model for 3D X-ray vascular reconstruction

Adaptive Markovian model for 3D X-ray vascular reconstructio...

引用

Conference on neural, Morphological, and stochastic methods in image and signal processing

作者： PAYOT, E PRETEUX, F GUILLEMAUD, R TROUSSET, Y LETI CEAF-38054 GRENOBLE 9FRANCE

ISBN: (纸本)0819419273

The Bayesian approach combined with the Markov random field approach provide a powerful and consistent mathematical framework for taking into account a priori knowledge and for regularizing ill-posed problems. Applied to 3D x-ray vascular reconstruction, such a combining approach requires a 3D object model describing the vascular tree. To take into account characteristic features of blood vessels, the proposed model performs a sort of shape analysis in order to estimate non-stationary parameters of the Markovian model. The global energy function is then expressed as a weighted combination of an adaptive smoothing potential which favors smoothing along the vessel direction; an enhancing potential which increases the contrast of small vessels; and a data-dependent term based on the difference between reprojection of the 3D reconstructed object and observed projections.

关键词： 3D modeling Diffusion Mathematical modeling X-rays Anisotropic diffusion Blood vessels Reconstruction algorithms image filtering Model-based design Smoothing

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