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neural and stochastic methods in image and signal processing

作者： Johnson, Valen E. Bowsher, James E. Qian, Jiang Jaszczak, Ronald J. Duke Univ. Durham NC USA

ISBN: (纸本)0819409391

This paper describes a statistical model for reconstruction of emission computed tomography (ECT) images. A distinguishing feature of this model is that it is parameterized in terms of quantities of direct physiological significance, rather than only in terms of grey-level voxel values. Specifically, parameters representing regions, region means, and region volumes are included in the model formulation and are estimated directly from projection data. The model is specified hierarchically within the Bayesian paradigm. At the lowest level of the hierarchy, a Gibbs distribution is employed to specify a probability distribution on the space of all possible partitions of the discretized image scene. A novel feature of this distribution is that the number of partitioning elements, or image regions, is not assumed known a priori. In contrast, any other segmentation models (e.g., Liang et al., 1991, Amit et al., 1991) require that the number of regions be specified prior to image reconstruction. Since the number of regions in a source distribution is seldom known a priori, allowing the number of regions to vary within the model framework is an important practical feature of this model. In the second level of the model hierarchy, random variables representing emission intensity are associated with each partitioning element or region. Individual voxel intensities are assumed to be drawn from a gamma distribution with mean equal to the region mean in the third stage, and in the final stage of the hierarchy projection data are assumed to be generated from Poisson distributions with means equal to weighted sums of voxel intensities.

关键词： image analysis

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Some new results in nonlinear predictive image coding using neural networks

Some new results in nonlinear predictive image coding using ...

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IEEE Workshop on neural Networks for signal processing

作者： H. Li Image Coding Group Department of Electrical Engineering Linkoping University Linkoping Sweden

The problem of nonlinear predictive image coding with multilayer perceptrons is considered. Some important aspects of coding, including the training of multilayer perceptrons, the adaptive scheme, and the robustness to the channel noise, are discussed in detail. Computer simulation results show that nonlinear predictors have better predictive performances than the linear DPCM. It is shown that the nonlinear predictor will produce smaller variance of predictive error than the linear predictor; that in the absence of the channel noise the nonlinear predictor can provide about a 3-dB improvement in signal-to-noise ratio over the linear one at the same transmission bit rate; and that, after being specially trained, the nonlinear predictor has a stronger robustness to the channel noise than the linear one.< >

关键词： Intelligent networks image coding neural networks Multilayer perceptrons Multi-layer neural network Mean square error methods Channel coding Pulse modulation Noise robustness Nonhomogeneous media

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Description and recognition of faces from 3D data

Description and recognition of faces from 3D data

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neural and stochastic methods in image and signal processing

作者： Coombes, Anne M. Richards, Robin Linney, Alfred D. Bruce, Vicki Fright, Rick Univ. Coll. London Crouch End London United Kingdom

ISBN: (纸本)0819409391

A method based on differential geometry, is presented for mathematically describing the shape of the facial surface. Three-dimensional data for the face are collected by optical surface scanning. The method allows the segmentation of the face into regions of a particular `surface type,' according to the surface curvature. Eight different surface types are produced which all have perceptually meaningful interpretations. The correspondence of the surface type regions to the facial features are easily visualized, allowing a qualitative assessment of the face. A quantitative description of the face in terms of the surface type regions can be produced and the variation of the description between faces is demonstrated. A set of optical surface scans can be registered together and averages to produce an average male and average female face. Thus an assessment of how individuals vary from the average can be made as well as a general statement about the differences between male and female faces. This method will enable an investigation to be made as to how reliably faces can be individuated by their surface shape which, if feasible, may be the basis of an automatic system for recognizing faces. It also has applications in physical anthropology, for classification of the face, facial reconstructive surgery, to quantify the changes in a face altered by reconstructive surgery and growth, and in visual perception, to assess the recognizability of faces. Examples of some of these applications are presented.

关键词： image analysis

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Statistical approaches to the tomographic reconstruction of finitely parameterized geometric objects

Statistical approaches to the tomographic reconstruction of ...

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neural and stochastic methods in image and signal processing

作者： Milanfar, Peyman Karl, William C. Willsky, Alan S. Massachusetts Inst. of Technology Cambridge MA USA

ISBN: (纸本)0819409391

Tomographic reconstruction in two dimensions is concerned with the reconstruction of a positive, bounded function f(x, y) and its compact domain of support ro from noisy and possibly sparse samples of its radon-transform projections, g(t, ro). If the pair (f, ro) is referred to as an object, a finitely parameterized object is one in which both f(x, y) and ro are determined uniquely by a finite number of parameters. For instance, a binary N-sided polygonal object in the plane is uniquely specified by exactly 2N parameters which may be the vertices, normals to the sides, etc. In this work we study the optimal reconstruction of finitely parameterized objects from noisy projections. In specific, we focus our study on the optimal reconstruction of binary polygonal objects from noisy projections. We show that when the projections are corrupted by Gaussian white noise, the optimal maximum likelihood (ML) solution to the reconstruction problem is the solution to a nonlinear optimization problem. This optimization problem is formulated over a parameter space which is a finite dimensional Euclidean space. We also demonstrate that in general, the moments of an object can be estimated directly from the projection data and that using these estimated moments, a good initial guess for the numerical solution to the nonlinear optimization problem may be constructed. Finally, we study the performance of the proposed algorithms from both statistical and computational viewpoints.

关键词： image reconstruction

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Robust identification of human face using mosaic pattern and BPN

Robust identification of human face using mosaic pattern and...

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IEEE Workshop on neural Networks for signal processing

作者： M. Kosugi NTT Human Interface Laboratories Yokosuka Kanagawa Japan

The backpropagation network (BPN) is applied to human face recognition. A mosaic pattern transformed from the central part of a human face image is put into the BPN for personal identification. This combination succeeds in recognition of hundreds of people with robustness not only for defocused or noisy images but also for images of different face expressions or different ages. Hidden units of the BPN extract peculiar and delicate features of the face, which cannot be obtained from existing statistical methods. A few hidden units can especially select only men or women. Moreover, a BPN with an additional unit for processing unfamiliar faces is proposed.< >

关键词： Robustness Humans Statistical analysis image edge detection neural networks Neodymium Laboratories Face recognition image recognition Feature extraction

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stochastic and neural methods in signal processing, image processing, and Computer Vision

Stochastic and Neural Methods in Signal Processing, Image Pr...

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stochastic and neural methods in signal processing, image processing, and Computer Vision 1991

The proceedings contain 46 papers. The topic discussed include: considering multiple-surface hypotheses in a Bayesian hierarchy;example of a bayes network of relations among visual features;recursive computation of a wire-frame representation of a scene from dynamic stereo using belief functions;hierarchical Dempster-Shafer evidential reasoning for image interpretation;standard-cell design architecture options below 5nm node: the ultimate scaling of FINFET and nanosheet;and small target detection for search and rescue operations using distributed deep learning and synthetic data generation.

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TIMBRE DISCRIMINATION OF signalS WITH IDENTICAL PITCH USING neural NETWORKS

TIMBRE DISCRIMINATION OF SIGNALS WITH IDENTICAL PITCH USING ...

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

作者： SAYEGH, S POMALAZARAEZ, C TEPPER, E BEER, B Purdue Univ. (United States)

Pitch recognition and timbre discrimination for a string instrument is investigated using artificial neural networks. Pitch recognition, the easier task, is realized with a linear classifier while timbre discriminatio... 详细信息

ISBN: (纸本)081940697X

关键词： neural networks signal processing image processing signal detection Artificial neural networks Computer vision technology Linear filtering Machine vision Multilayers stochastic processes

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UTILIZING THE CENTRAL-LIMIT-THEOREM FOR PARALLEL MULTIPLE-SCALE image-processing WITH neural ARCHITECTURES

UTILIZING THE CENTRAL-LIMIT-THEOREM FOR PARALLEL MULTIPLE-SC...

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

作者： BENARIE, J Illinois Institute of Technology (United States)

ISBN: (纸本)081940697X

A set of neural lattices that are based on the central limit theorem is described. Each of the described lattices, generates in parallel a set of multiple scale Gaussian smoothing of their input arrays. The recursive smoothing principle of the lattices can be extended to any dimension. In addition, the lattices can generate in real time a variety of multiple scale operators such as Canny's edge detectors, Laplacians of Gaussians, and multidimensional sine, cosine, Fourier, and Gabor transforms.

关键词： Smoothing image processing Convolution 3D image processing Transform theory signal processing Computer vision technology Machine vision neural networks stochastic processes

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APPLICATION OF neural NETWORK TO RESTORATION OF signalS DEGRADED BY A stochastic, SHIFT-VARIANT IMPULSE-RESPONSE FUNCTION AND ADDITIVE NOISE

APPLICATION OF NEURAL NETWORK TO RESTORATION OF SIGNALS DEGR...

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

作者： BILGEN, M HUNG, HS Iowa State Univ. (United States)

ISBN: (纸本)081940697X

An artificial neural network is adopted for estimating discrete (sampled in time and quantized in amplitude) signals degraded by a stochastic, shift-variant impulse response (blur) function in the presence of noise. The signal restoration problem is formulated as a combinatorial optimization problem wherein a nonlinear cost function, termed stochastic constrained restoration error energy, is to be minimized. By matching the cost function with the energy function of the associated neural network, the interconnection strengths and bias inputs of the neural network are related to the degraded signal, blur statistics, and constraint parameters. The solution which minimizes the energy function of the neural network is thus obtained iteratively by the simulated annealing algorithm. Simulation results show the effectiveness of the proposed algorithm which has, in addition, the capability of imposing level constraints on the original signal.

关键词： stochastic processes neural networks Algorithms Neurons Interference (communication) image processing Computer vision technology Machine vision signal processing Data modeling

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BLIND SEPARATION OF SOURCES .2. PROBLEMS STATEMENT

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signal processing 1991年第1期24卷 11-20页

作者： COMON, P JUTTEN, C HERAULT, J THOMSON-SINTRA Parc de Sophia Antipolis BP 138 F-06561 Valbonne Cedex France Laboratory TIRF INPG 46 av. Felix Viallet F-38031 Grenoble France

Though it arouses more and more curiosity, the HJ iterative algorithm has never been derived in mathematical terms to date. We attempt in this paper to describe it from a statistical point of view. For instance the updating term of the synaptic efficacies matrix cannot be the gradient of a single C2 functional contrary to what is sometimes understood. In fact, we show that the HJ algorithm is actually searching common zeros of n functionals by pipelined stochastic iterations. Based on simulation results, advantages and limitations as well as possible improvements are pointed out after a short theoretical analysis.

关键词： signal AND image processing stochastic PROCESSES MIXTURE neural NETWORKS PRINCIPAL COMPONENTS INDEPENDENT COMPONENTS INVERSE PROBLEM

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