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Annual IEEE Symposium on computer-Based Medical Systems

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

The coronary arteries may be represented and visualized efficiently by a generalized cylinder (GC) model with elliptical cross-sections. By using this model and utilizing quadratic expression of an ellipse, the intensity distribution of an ellipse in any image plane, which is perpendicular to the plane of the arterial cross-section, is computed. A nonlinear parametric model for observed intensity distribution of an arterial cross-section is applied to estimate not only cross-sectional areas but also parameters of the ellipse which are essential in 3-D reconstruction of an arterial segment. This model takes into account background intensity, noise and blurring. Performance of this model has been tested on computer-generated and actual data for various background types and noise values and compared to those of other methods for estimating arterial dimensions. The computed cross-sectional areas from our model for computer-generated as well as actual data demonstrate much less variability than those reported in recent literature. 3-D reconstruction of arterial segments from actual biplane angiograms is presented.< >

关键词： Three dimensional displays Arteries Background noise Diseases Parameter estimation Visualization Parametric statistics image reconstruction Shape Bones

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An analysis of Monte Carlo methods for likelihood estimation of Gibbsian images

An analysis of Monte Carlo methods for likelihood estimation...

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1993 IEEE International Conference on Acoustics, Speech, and Signal Processing

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

A unified analysis of two Monte Carlo algorithms for estimating the likelihood function of Gibbs random field images is presented. It is shown that such an estimation reduces to estimating the partition functions of suitably chosen Gibbs random fields. The first algorithm requires drawing samples from a mutually compatible Gibbs random field and provides unbiased and consistent estimators of these partition functions. The second algorithm uses samples which are approximately drawn from the Gibbs distribution, and results in asymptotically unbiased and consistent estimators of the partition functions. A measure of the computational complexity of these algorithms which makes it possible to compare them is introduced. It is concluded that the first algorithm is superior, especially for models with strong interactions.< >

关键词： image analysis Partitioning algorithms Monte Carlo methods Computational complexity Testing Laboratories image processing Parameter estimation Scattering Algorithm design and analysis

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Integrated adaptive fuzzy clustering (IAFC) algorithm

Integrated adaptive fuzzy clustering (IAFC) algorithm

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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

An integrated adaptive fuzzy clustering (IAFC) algorithm using a structure similar to that found in the Adaptive Resonance Theory (ART-1) neural network, is presented. The IAFC incorporates a new learning rule and a new similarity measure to eliminate some structural problems inherent in other fuzzy ART-type neural networks. The new learning rule utilizes a fuzzy membership value, a function of the number of iterations, and a fuzzy within-cluster membership value. The new similarity measure incorporates a fuzzy membership value to the Euclidean distance. This incorporation of the new learning rule and the new similarity measure guarantees the convergence of weights in the IAFC algorithm and provides more flexibility to the shapes of the clusters formed by this algorithm. The critical parameters in the operation of IAFC are discussed. The performance of IAFC is evaluated in the classification of real data and compared with other recent neuro-fuzzy clustering algorithms.< >

关键词： Clustering algorithms Neural networks Fuzzy neural networks Euclidean distance Partitioning algorithms Fuzzy sets Resonance Shape measurement Convergence Subspace constraints

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A robust method for wideband signal separation

A robust method for wideband signal separation

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： V.C. Soon L. Tong Y.F. Huang R. Liu Laboratory for Image and Signal Analysis (LISA) Department of Electrical Engineering University of Notre Dame Notre Dame IN USA Department of Electrical and Computer Engineering West Virginia University Morgantown WV USA

A novel approach to solving the problem of signal separation under model uncertainties and unknown source signal characteristics is proposed. The approach features the incorporation of blind identification with clustering techniques. The approach is capable of estimating source locations and source signals under various uncertain conditions including unknown sensor gains, unknown combinations of near-field and far-field sources, unknown combinations of wideband and narrowband sources, unknown source spectral characteristics (their spectra may be overlapping or non-overlapping), and unknown number of signals.< >

关键词： Robustness Wideband Source separation Sensor arrays Sensor phenomena and characterization Uncertainty Direction of arrival estimation Narrowband Phased arrays Speech enhancement

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Interactive tools for signal processing education. The signal processing instructional facility (SPIF lab) at Washington State University

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computer Applications in engineering Education 1993年第6期1卷 517-517页

作者： Bamberger, R.H. Signal Processing Instructional Facility School of Electrical Engineering and Computer Science Washington State University Pullman WA 99164-2752 Dr. Roberto H. Bamberger:was born on January 30 1965 in Buenos Aires Argentina. He received his B.E.E and PhD from the Georgia Institute of Technology in 1986 and 1990 respectively. Currently Dr. Bamberger is an assistant professor in the School of Electrical Engineering and Computer Science at Washington State University. Dr. Bamberger's research program is focused on the design implementation and application of multidimensional multirate filter banks subband image and video compression analysis of nonstationary data and application of digital signal processing techniques in the design of analog and mixed-mode integrated circuits. Dr. Bamberger is the founder and director of the Signal Processing Instructional Facility (SPIF Lab) at Washington State University. The SPIF Lab features interactive multimedia-based tools for teaching concepts related to linear systems theory. Dr. Bamberger is active in developing multimedia-based instructional laboratories for a variety of other topics as well. In addition to his teaching and research programs Dr. Bamberger is an associate editor for theIEEE Transactions on Signal Processingand a member of the editorial board forComputer Applications in Engineering Education. Dr. Bamberger is also advisor to the WSU Student Branch of the IEEE and assistant director of the WSU Cougar Marching Band. Dr. Bamberger is a member of Phi Eta Sigma Eta Kappa Nu Tau Beta Pi and Kappa Kappa Psi.

The Signal Processing Instructional Facility (SPIF Lab) is an experiment in using in interactive multimedia for teaching concepts related to linear systems theory and signal processing. The goals of the SPIF lab are to augment, enhance, and interconnect sophomore, junior, and senior level courses with the common thread of linear systems and transforms by unifying the experimentation medium. In this fashion, physical phenomenon is returned to the forefront of engineering education. The laboratory features powerful Mathematica Notebooks (a form of hypertext) and interactive applications that use dedicated DSP microprocessors.

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ADAPTIVE FUZZY LEADER CLUSTERING OF COMPLEX DATA SETS IN PATTERN-RECOGNITION

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1992年第5期3卷 794-800页

作者： NEWTON, SC PEMMARAJU, S MITRA, S Computer Vision and Image Analysis Lab Department of Electrical Engineering Texas Tech University Lubbock TX USA

Most real data structures encountered in speech and image recognition and in medical and many other decision making tasks are quite complex in nature and rather difficult to organize for designing autonomous and optimal control and recognition systems. This paper presents a modular, unsupervised neural network architecture which can be used for clustering and classification of complex data sets. The adaptive fuzzy leader clustering (AFLC) architecture is a hybrid neural-fuzzy system which learns on-line in a stable and efficient manner. The system uses a control structure similar to that found in the adaptive resonance theory (ART-1) network to identify the cluster centers initially. The initial classification of an input takes place in a two-stage process: a simple competitive stage and a distance metric comparison stage. The cluster prototypes are then incrementally updated by relocating the centroid positions from fuzzy C-means (FCM) system equations for the centroids and the membership values. The operational characteristics of AFLC and the critical parameters involved in its operation are discussed. The performance of the AFLC algorithm is presented through application of the algorithm to the Anderson Iris data and laser-luminescent fingerprint image data. The AFLC algorithm successfully classifies features extracted from real data, discrete or continuous, indicating the potential strength of this new clustering algorithm in analyzing complex data sets.

关键词： Representation of data Fuzzy Taxonomy Centroid Self tuning Clustering algorithms Pattern recognition Dataset Cluster analysis

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Morphological analysis of discrete random shapes

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Journal of Mathematical Imaging and Vision 1992年第2-3期2卷 193-215页

作者： Goutsias, John Image Analysis and Communications Laboratory Department of Electrical and Computer Engineering Johns Hopkins University Baltimore 21218 MD United States

In this paper I discuss a number of theoretical issues regarding the morphological analysis of discrete random shapes by means of Matheron's random set theory. I revisit this theory by limiting myself to the discrete case, since most image data are available in a discrete form. Although it may seem that the transition from the continuous to the discrete case is straightforward (since most of Matheron's theory is general enough to incorporate the discrete case as a special case), this transition is often challenging and full of exciting and, surprisingly, pleasant results. I introduce the concept of the cumulative-distribution functional of a discrete random set and review some fundamental properties of the capacity functional (a fundamental statistical quantity that uniquely defines a random set and relates random set theory to mathematical morphology). In analogy to a recent result and under a natural boundness condition, I show that there exists a one-to-one correspondence between the probability-mass function of a discrete binary random field and the corresponding cumulative-distribution functional. The relationship between the cumulative-distribution functional and the capacity functional of a discrete random set is also established. The cumulative-distribution and capacity functionals are related to the higher-order moments of a discrete binary random field, and, therefore, their computation is equivalent to computing these moments. A brief discussion of how to perform such computations for a certain class of discrete random sets is provided. The capacity functional of a morphologically transformed, continuous random set cannot be associated to the capacity functional of the random set itself, except in the case of dilation. I show that the derivation of such an association is possible in the discrete case and for the cases of dilation and erosion and more complicated morphological transformations, such as opening and closing. These relationships are then used

关键词： binary random fields discrete random shapes mathematical morphology random set theory shape analysis

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IDENTIFIABILITY OF A SET OF QUADRATIC EQUATIONS WITH UNKNOWN COEFFICIENTS

IDENTIFIABILITY OF A SET OF QUADRATIC EQUATIONS WITH UNKNOWN...

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INTERNATIONAL SYMP ON CIRCUITS AND SYSTEMS ( ISCAS 92 )

作者： TONG, L YU, S LIU, R Department of Electrical and Computer Engineering West Virginia University Morgantown 26506 WV United States Laboratory for Image and Signal Analysis(LISA) Department of Electrical Engineering University of Notre Dame Notre Dame 46556 IN United States

ISBN: (纸本)0780305930

In this paper, the motivation for studying the simultaneous quadratic equations with unknown coefficients is first presented. Its indeterminacy and identifiability are considered. A main lemma and three theorems are then presented. A comparison with the well known simultaneous diagonalization problem is also given. © 1992 IEEE.

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An adaptive fuzzy system for control and clustering of arbitrary data patterns

An adaptive fuzzy system for control and clustering of arbit...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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

A modular, unsupervised neural network architecture is described. It can be used for data clustering and classification. The adaptive fuzzy leader clustering (AFLC) architecture is a hybrid neural-fuzzy system which learns online in a stable and efficient manner. The system consists of a fuzzy k-means learning rule embedded within a control structure similar to that found in the adaptive resonance theory (ART-1) network. AFLC adaptively clusters analog inputs into classes without prior knowledge of the entire data set or of the number of clusters present in the data. The classification of an input takes place in a two-stage process; a simple competitive stage and a euclidean metric comparison stage. The AFLC algorithm and its operating characteristics are described. The algorithm is compared to an adaptive Bayesian classifier for some real data.< >

关键词： Adaptive systems Programmable control Adaptive control Fuzzy systems Control systems Clustering algorithms Neural networks Fuzzy control Resonance Euclidean distance

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Identifiability of a quadratic equations with unknown coefficients

Identifiability of a quadratic equations with unknown coeffi...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： L. Tong S. Yu R. Liu Department of Electrical and Computer Engineering West Virginia University Morgantown WV USA Laboratory for Image and Signal Analysis Department of Electrical Engineering University of Notre Dame Notre Dame IN USA

A set of simultaneous quadratic equations with unknown coefficients is considered. The motivation for studying the equations is presented. Indeterminacy and identifiability are considered. A main lemma and three theor... 详细信息

关键词： Equations Bismuth Laboratories image analysis Signal analysis Array signal processing Blind equalizers Additive noise Statistics

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