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A study of pyramidal techniques for image representation and compression

Journal of Visual Communication and image Representation 1994年第2期5卷 190-203页

作者： Kong, Xuan Goutsias, John Department of Electrical Engineering Northern Illinois University De Kalb IL 60115 United States Department of Electrical and Computer Engineering Image Analysis and Communications Laboratory Johns Hopkins University Baltimore MD 21218 United States

We review and compare a number of linear and nonlinear pyramidal image decomposition techniques which are based on the approach proposed by Burt and Adelson (IEEE Trans. Comm. 31, 1983, 532-540). We argue that the design of a proper Burt-Adelson type pyramidal decomposition technique is directly related to the design of an optimal (nonlinear in general) image predictor. However, determining such a predictor is not possible in general. We propose four prediction constraints, which uniquely identify the “optimal” predictor as being a morphological opening. This choice naturally leads to one of the morphological image decomposition algorithms proposed by Heijmans and Toet (Comput. vision Graphics image Process. image Understanding, 54, 1991, 384-400). By using an experimental analysis, based on a collection of four images with a broad spectrum of structural detail, we are able to study the behavior of six pyramidal image representation and compression techniques, and demonstrate the superiority (in terms of compression performance and computational simplicity) of the Heijmans-Toet algorithm. © 1994 Academic Press. All rights reserved.

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New morphological operators: BERD and BDRE

New morphological operators: BERD and BDRE

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IEEE Southwest Symposium on image analysis and Interpretation

作者： A. Kher S. Mitra Computer Vision and Image Analysis Laboratory Department of Electrical Engineering Texas Tech University Lubbock TX USA

The authors briefly describe their new morphological operators of "boundary erosion region dilation (BERD)" and "boundary dilation region erosion (BDRE)". The paper covers definitions, properties, and some of the applications of these operators. BERD and BDRE operations may be used to approximately perform the job of various common morphological filtering operations with lesser computational times. BERD and BDRE operations with 2-D structuring elements may be used to construct robust connectivity preserving filters. Unlike commonly used median closing and opening operators/sup 1,5,6/, these filters do not destroy thin regions which rapidly change their direction or the regions which can not be probed by straight lines in limited directions. They have used these connectivity preserving filters for removing speckle noise from SAR images while retaining thin regions and fine details of regions boundaries.< >

关键词： Morphological operations Filters image analysis image processing Filtering Morphology Pixel computer vision Laboratories Noise robustness

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Handwritten character recognition by an adaptive fuzzy clustering algorithm

Handwritten character recognition by an adaptive fuzzy clust...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： A. Sharan S. Mitra Computer Vision and Image Analysis Laboratory Department of Electrical Engineering Texas Tech University Lubbock TX USA

Unconstrained handwritten characters pose a serious challenge to the development of a recognition algorithm. Many approaches have been studied over the years for such a recognition algorithm. We use an adaptive neuro-fuzzy clustering algorithm for classification and recognition of handwritten characters of a variety of styles and investigate the effectiveness of Fourier coefficients as representative features of handwritten characters in the presence of noise. Our results indicate that the adaptive clustering algorithm outperforms k-means clustering in handwritten character recognition for the same data representation. However some misclassifications cannot be avoided due to inherent problems associated with large variability in handwriting styles and the presence of excessive noise in practice.< >

关键词： Character recognition Clustering algorithms Handwriting recognition Writing Humans Euclidean distance computer vision image analysis Laboratories image recognition

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Fuzzy inferencing with ART networks

Fuzzy inferencing with ART networks

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International Joint Conference on Neural Networks (IJCNN)

作者： S. Mitra Computer Vision and Image Analysis Laboratory Department of Electrical Engineering Texas Tech University Lubbock TX USA

A recent trend in developing adaptive decision models has been to integrate the concept of fuzzy membership functions of data samples with adaptive learning inherent to neural nets. Several different approaches have been suggested for such integration involving Adaptive Resonance Theory (ART) as well as Kohonen self-organizing neural networks. Such neuro-fuzzy models appear to be quite effective in successful clustering of complex data samples encountered in many pattern recognition and control applications where traditional decision models fail due to lack of knowledge of data distributions and unavailability of training data sets. The strengths and weaknesses of currently existing ART-based neuro-fuzzy models are described.< >

关键词： Subspace constraints Clustering algorithms Neural networks Fuzzy neural networks Euclidean distance computer vision image sequence analysis Laboratories Resonance Pattern recognition

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Maximum likelihood parameter estimation for non-Gaussian prior signal models

Maximum likelihood parameter estimation for non-Gaussian pri...

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The 1994 1st IEEE International Conference on image Processing

作者： Schultz, R.R. Stevenson, R.L. Lumsdaine, A. Laboratory for Image and Signal Analysis Department of Electrical Engineering University of Notre Dame Notre DameIN46556 United States Department of Computer Science and Engineering University of Notre Dame Notre DameIN46556 United States

For signals containing discontinuities, the usual assumptions of Gauss-Markov distributed signal sources do not hold. To preserve edges, non-Gaussian prior models have been developed for use in Bayesian restoration. These models are generally dependent upon two parameters, one controlling the size of reconstructed discontinuities, and the other controlling data smoothing. The authors propose a maximum likelihood technique for automatically estimating these parameters, resulting in the optimization of an expression dependent upon the prior model partition function. An exact expression is derived for the 1D signal model partition function, while an approximation is proposed for the 2D image model partition function. Parameters estimated from degraded signals result in high quality restorations. © 1994 IEEE.

关键词： Restoration

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Motion perception using spatiotemporal frequency analysis

Motion perception using spatiotemporal frequency analysis

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IEEE International Conference on image Processing

作者： G. Ravichandran M.M. Trivedi Computer Vision and Robotics Research Laboratory Electrical and Computer Engineering Department Computer Vision and Robotics Research Laboratory Electrical and Computer Engineering Department University of Tennessee Knoxville TN USA

Motion information is extracted by identifying the temporal signature associated with the textured objects in the scene. We present a new computational framework for motion perception. Our methodology considers spatiotemporal frequency (STF) domain analysis to extract the optical flow information. First, we show that a sequence of image frames can be used to extract the motion parameters for the different regions in a dynamic scene using the basic Fourier transform properties in the STF analysis approach. A detailed analytical description of this model to interchangably extract motion and depth parameters and results to highlight their salient properties are presented.< >

关键词： Motion analysis Spatiotemporal phenomena image motion analysis Data mining Layout Frequency domain analysis Information analysis Fourier transforms image analysis image sequence analysis

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analysis of texture images using robust fractal description

Analysis of texture images using robust fractal description

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IEEE Southwest Symposium on image analysis and Interpretation

作者： N. Avadhanam S. Mitra Center for Image Processing & Integrated Computing Department of Electrical & Computer Engineering University of California Davis CA USA Computer Vision and Image Analysis Lab Department of Electrical Engineering Texas Tech University Lubbock TX USA

A maximum likelihood estimation (MLE) method is used to estimate the fractal dimension of a number of natural texture images with and without the presence of noise. An additional texture measure which can be linked to the lacunarity measure is used to characterize natural textures since fractal dimension alone cannot totally characterize texture images. Segmentation of natural textures is successfully achieved by a k-means clustering algorithm using fractal dimension and the additional measure as representative features.< >

关键词： image analysis image texture analysis Robustness Fractals Maximum likelihood estimation image segmentation Polymers image motion analysis computer vision Clustering algorithms

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Stochastic pyramids for multiscale signal synthesis and analysis

Stochastic pyramids for multiscale signal synthesis and anal...

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IEEE International Conference on image Processing

作者： J. Goutsias Department of Electrical and Computer Engineering Image Analysis and Communications Laboratory Johns Hopkins University Baltimore MD USA

Multiscale techniques require that signals are fitted to pyramid structures, with each level of the pyramid corresponding to a reduced-resolution approximation of the signal. Unlike deterministic pyramids, stochastic pyramids can be applied to signals characterized by some form of uncertainty. A number of fundamental properties of stochastic pyramids are studied, and advantages and disadvantages of various pyramid structures are discussed. Furthermore, the stochastic pyramid transform is proposed, as a solution to all problems associated with traditional stochastic pyramids. We briefly argue that this transform naturally leads to the multigrid Monte Carlo method, proposed by Goodman and Sokal (1989), which is mainly used to generate Markov random field images.< >

关键词： Stochastic processes Signal synthesis Signal analysis Random sequences Signal processing image sequence analysis Signal representations Signal resolution Probability density function Signal processing algorithms

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Piecewise and local class models for image restoration

Piecewise and local class models for image restoration

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IEEE International Conference on image Processing

作者： S.T. Acton A.C. Bovik School of Electrical Computer Engineering Oklahoma State University Stillwater OK USA Laboratory for Vision SystemsCenter for Vision and Image Science Department of Electrical Computer Engineering University of Texas Austin Austin TX USA

In this paper, we present a new approach to image restoration based on a flexible constraint framework that encapsulates structural assumptions about the uncorrupted image. Piecewise and local class (PALC) models are defined and utilized to restore images degraded by linear blurring and additive noise. The restoration process is accomplished by iteratively deconvolving the solution image while simultaneously optimizing local image characteristics defined by the PALC models. Solution images to this ill-posed, combinatorial problem are computed using the novel generalized deterministic annealing (GDA) optimization technique. The results demonstrate high quality image restoration as measured by local feature integrity, improvement in signal-to-noise ratio, and reduction of restoration artifacts, especially in the presence of heavy-tailed additive noise.< >

关键词： image restoration Additive noise Degradation image edge detection Deconvolution Bismuth computer vision Machine vision image enhancement Maximum likelihood estimation

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Processing incomplete and uncertain data using subspace methods

Processing incomplete and uncertain data using subspace meth...

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International Conference on Pattern Recognition

作者： C.-F. Westin H. Knutsson Department of Electrical Engineering Computer Vision Laboratory Linkoping University Sweden

An approach for processing incomplete or uncertain data based on subspace methods is presented in this paper. The paper addresses the problem of how a subset of a parameter vector describing a signal can be estimated. The term parameter vector refers to the coefficients in the linear combination of basis functions describing a local image neighbourhood. images are normally described locally using simple basis functions. Low order local momentums such as order 0 (the local DC component), 1 and 2 are commonly used. Low order differentiations are also useful descriptors. In densely regularly sampled images, these descriptors are easily computed using standard convolution. However, when working with irregularly sampled data or incomplete data the signal model has to be of higher order than the signal variations of interest. This is the case where only a part of the parameter vector is to be estimated. If possible, only this part of the parameter should be calculated explicitly as opposed to calculating the whole parameter vector. This paper describes such a method based on partitioning the model subspace into two parts.

关键词： Convolution Vectors Filtering Interpolation image texture analysis computer vision Laboratories Data preprocessing Tensile stress image edge detection

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