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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： J.D. Paola R.A. Schowengerdt Digital Image Analysis Laboratory Department of Electrical and Computer Engineering University of Arizona Tucson Tucson AZ USA

The authors have classified four different images, under various levels of JPEG compression, using the following classification algorithms: minimum-distance, maximum-likelihood, and neural network. The training site accuracy and percent difference from the original classification were tabulated for each image compression level, with maximum-likelihood showing the poorest results. In general, as compression ratio increased, the classification retained its overall appearance, but much of the pixel-to-pixel detail was eliminated. The authors also examined the effect of compression on spatial pattern detection using a neural network.

关键词： image coding image classification Transform coding Neural networks Discrete cosine transforms Remote sensing Classification algorithms image storage Discrete transforms Quantization

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On balanced realizations of 2-D delta-operator formulated discrete-time systems

On balanced realizations of 2-D delta-operator formulated di...

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Southcon

作者： K. Premaratne M.M. Ekanayake P.H. Bauer Department of Electrical and Computer Engineering University of Miami Coral Gables FL USA Department of Electrical Engineering Laboratory of Image and Signal Analysis University of Notre Dame Notre Dame IN USA

Delta-operator based implementations can avoid the numerical ill-conditioning usually associated with the high speed shift-operator based implementations of discrete-time systems. Moreover, it provides a unified methodology for tackling both continuous- and discrete-time systems. In particular, it has been shown that, delta-operator based balanced realizations can offer superior coefficient sensitivity properties under fixed-point arithmetic. The authors address computation of balanced realizations. For this purpose, given a discrete-time system, the relationship between its shift- and delta-operator formulated balanced realizations is presented.

关键词： Fixed-point arithmetic Computational modeling Sampling methods Polynomials Laboratories Signal analysis Computed tomography Eigenvalues and eigenfunctions Gold Stability

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OPTIMAL BRIGHTNESS FUNCTIONS FOR OPTICAL-FLOW ESTIMATION OF DEFORMABLE MOTION

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IEEE TRANSACTIONS ON image PROCESSING 1994年第2期3卷 178-191页

作者： DENNEY, TS PRINCE, JL Image Analysis and Communications Laboratory Department of Electrical and Computer Engineering Johns Hopkins University Baltimore MD USA

Estimation accuracy of Horn and Schunck's classical optical Bow algorithm depends on many factors including the brightness pattern of the measured images. Since some applications can select brightness functions with which to ''paint'' the object, it is desirable to know what patterns will lead to the best motion estimates. In this paper we present a method for determining this pattern a priori using mild assumptions about the velocity field and imaging process. Our method is based on formulating Horn and Schunck's algorithm as a linear smoother and rigorously deriving an expression for the corresponding error covariance function. We then specify a scalar performance measure and develop an approach to select an optimal brightness function which minimizes this performance measure from within a parametrized class. Conditions for existence of an optimal brightness function are also given. The resulting optimal performance is demonstrated using simulations, and a discussion of these results and potential future research is given.

关键词： Brightness image motion analysis Motion estimation Fluid flow measurement Optical imaging Magnetic resonance imaging Motion measurement Computational modeling Standards development

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TOMOGRAPHIC RECONSTRUCTION OF 3-D VECTOR-FIELDS USING INNER-PRODUCT PROBES

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IEEE TRANSACTIONS ON image PROCESSING 1994年第2期3卷 216-219页

作者： PRINCE, JL Image Analysis and Communications Laboratory Department of Electrical and Computer Engineering Johns Hopkins University Baltimore MD USA

Inner product probe measurements are defined for tomographic reconstruction of 3-D vector fields. It is shown that one set of measurements is required to reconstruct an irrotational field, two are required to reconstr... 详细信息

关键词： Tomography Probes image reconstruction Sea measurements Force measurement Ultrasonic imaging Performance evaluation Graphics Acoustic imaging Oceans

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A study of pyramidal techniques for image representation and compression

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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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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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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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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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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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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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