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EARLY ORIENTATION SELECTION - TANGENT FIELDS AND THE DIMENSIONALITY OF THEIR SUPPORT

computer vision GRAPHICS AND image PROCESSING 1985年第1期32卷 74-103页

作者： ZUCKER, SW Computer Vision and Robotics Laboratory Department of Electrical Engineering McGill University Montréal Québec Canada

Orientation selection is the inference of orientation information out of images. It is one of the foundations on which other visual structures are built, since it must precede the formation of contours out of pointillist data and surfaces out of surface markings. We take a differential geometric view in defining orientation selection, and develop algorithms for actually doing it. The goal of these algorithms is formulated in mathematical terms as the inference of a vector field of tangents (to the contours), and the algorithms are studied in both abstract and computational forms. They are formulated as matching problems, and algorithms for solving them are reduced to biologically plausible terms. We show that two different matching problems are necessary, the first for 1-dimensional contours (which we refer to as Type I processes) and second for 2-dimensional flows (or Type II processes). We conjecture that this difference is reflected in the response properties of “simple” and “complex” cells, respectively, and predict several other psychophysical phenomena.

关键词： vision

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Improved pitch detection algorithm for noisy speech

Improved pitch detection algorithm for noisy speech

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Sanguoon Chung V. Algazi Signal and Image Processing Laboratory Department of Electrical and Computer Engineering University of California Davis CA USA

We propose a new, robust pitch detection algorithm for speech degraded by additive noise. Our algorithm exploits the high correlation between adjacent pitch periods that does not exist for the segment as a whole and performs well in the vicinity of voiced/unvoiced regions where the local SNR is low. The algorithm works as follows. We first determine an estimate of the pitch period near the short time peak energy where the local SNR is highest. We then adaptively estimate the local pitch period from the peak towards the transition region by using pitch synchronous cross-correlation with an updated waveform. The performance of this new algorithm is compared to the SIFT and CEPSTRUM algorithms.

关键词： Detection algorithms Speech enhancement Cepstrum Acoustic noise Working environment noise Detectors Cepstral analysis Algorithm design and analysis Time measurement Speech synthesis

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LEARNING CHARACTERISTICS OF STOCHASTIC-GRADIENT-DESCENT ALGORITHMS - A GENERAL STUDY, analysis, AND CRITIQUE

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SIGNAL PROCESSING 1984年第2期6卷 113-133页

作者： GARDNER, WA Signal and Image Processing Laboratory Department of Electrical and Computer Engineering University of California Davis 95616 USA

A comprehensive analysis of the mean-square learning characteristics of stochastic-descent algorithms is presented. The approach is based on the commonly exploited simplifying assumption of stationary independent training vectors. Characteristics analyzed include stability, steady-state misadjustment, initial rate of convergence, optimum step size, and steady-state autocovariance and spectral characteristics of the weight-vector. Effects on these characteristics due to degree of randomness of stochastic gradient, particular data distribution, and data corruption are isolated and analyzed. An objective of the work is to keep the number of simplifying assumptions and approximations to a minimum. Comparison of results with previous more approximate analyses are made. Lernkurven von stochastischen Gradienten Algorithmen werden untersucht. Die vereinfachende Annahme von stationären, unabhängigen Trainingsvektoren wird benutzt. Charakteristiken die untersucht werden beinhalten Stabilität, ‘steady state’ Fehlanpassung, Start Konvergenz, optimaler Schritt sowie ‘steady state’ Autokovarianz und spektrale Charakteristik des Gewichtsvektors. Die Effekten auf diese Charakteristiken von dem Zufälligkeitsgrad des stichastischen Gradienten, besonderer Daten Verteilung und Daten Verderbung werden isoliert und analysiert. Ein Zielpunkt dieser Arbeit ist es die Anzahl vereinfachenden Annahmen und Approximationen auf ein minimum zu beschränken. Vergleiche mit Resultaten die von approximativeren Analysen stammen werden gezogen. Une analyse d'ensemble des caractéristiques de convergence de l'erreur quadratique moyenne dans les algorithmes utilisant la décroissance du gradient stochastique est presentée. Cette approche est basée sur l'hypothése simplificatrice classique de stationnarité et indépendance des vecteurs de test. Les caractéristiques analysées comprennent la stabilité, l'écart d'état stable, la vitesse initiale de convergence, le pas optimum d'incrémentation, et l'autocovarian

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SEGMENTATION OF A HIGH-RESOLUTION URBAN SCENE USING TEXTURE OPERATORS

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computer vision GRAPHICS AND image PROCESSING 1984年第3期25卷 273-310页

作者： CONNERS, RW TRIVEDI, MM HARLOW, CA Remote Sensing and Image Processing Laboratory Department of Electrical and Computer Engineering Louisiana State University Baton Rouge Louisiana 70803

A study aimed at segmenting a high-resolution black and white image of Sunnyvale, California, is described. In this study regions were classified as belonging to any one of nine classes: residential, commercial/industrial, mobile home, water, dry land, runway/taxiway, aircraft parking, multilane highway, and vehicle parking. The classes were selected so that they directly relate to the Defense Mapping Agency's Mapping, Charting and Geodesy tangible features. To attack the problem a statistical segmentation procedure was devised. The primitive operators used to drive the segmentation are texture measures derived from cooccurrence matrices. The segmentation procedure considers three kinds of regions at each level of the segmentation: uniform, boundary, and unspecified. At every level the procedure differentiates uniform regions from boundary and unspecified regions. In the assigns a class label to the uniform regions. The boundary and unspecified regions are split to form higher level regions. The methodologies involved are mathematically developed as a series of hypothesis tests. While only a one-level segmentation was performed studies are described which show the capabilities of each of these hypothesis tests. In particular an 83% correct classification was obtained in testing the labeling procedure. These studies indicate that the proposed procedure should be useful for land use classifications as well as other problems.

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Identification of unique objects in high resolution aerial images

Identification of unique objects in high resolution aerial i...

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Applications of Artificial Intelligence I 1984

作者： Trivedi, Mohan M. Harlow, Charles A. Remote Sensing and Image Processing Laboratory Department of Electrical and Computer Engineering Louisiana State University Baton RougeLA70803 United States

Accurate and reliable detection of unique objects is an important component of an image understanding system. The objects are considered to have a unique pattern and should be recognized based upon their own characteristics. An object detection approach applicable to the high resolution aerial images is developed in the paper. The methodologies incorporate features such as minimal sensitivity to the backgrounds on which objects may appear, ability to detect objects appearing in arbitrary orientations, and use of a unified set of operators to analyze various levels of details. Specifically the gray level cooccurrence matrix is used to provide the early vision operators. Results of several experiments show the promise for the prpposed methods. © 1984 SPIE. All rights reserved.

关键词： Object detection

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Radon transform theory for random fields and optimum image reconstruction from noisy projections

Radon transform theory for random fields and optimum image r...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： A. Jain S. Ansari Signal and Image Processing Laboratory Department of Electrical and Computer Engineering University of California Davis CA USA

In this paper we present some new results on Radon transform theory for stationary random fields. In particular we present a new projection theorem which gives the relation between the power spectrum density of one dimensional projections of a stationary random field and its two dimensional power spectrum density. This result yields the optimum mean square reconstruction filter from noisy projections and is useful in other problems such as multidimensional spectral estimation from one dimensional projections, noise analysis in computed tomography, etc. Example are given to demonstrate the usefulness of these results.

关键词： image reconstruction Filters Frequency response Computed tomography Fourier transforms Convolution Stochastic processes Signal processing image processing Laboratories

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Application of a simple hearing model to the design of audio filters

Application of a simple hearing model to the design of audio...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： M. Ready V. Algazi Signal and Image Processing Laboratory Department of Electrical and Computer Engineering University of California Davis CA USA

Filtering audio signals with filters designed exclusively from frequency domain specifications may result in an audible distortion in the vicinity of sharp amplitude transitions. This paper considers the application of known psychoacoustical properties to the design of digital audio filters which minimizes this distortion while approximating some ideal frequency domain characteristics. Psychoacoustic properties and a simple model for hearing are reviewed. A weighted least squares design criteria based on the model and frequency domain specifications is given. Examples of FIR and IIR filters are given and compared to classical frequency domain filters.

关键词： Auditory system Finite impulse response filter Frequency domain analysis Psychoacoustic models IIR filters Psychology Filtering Signal design Distortion Digital filters

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FAST ALGORITHMS FOR ESTIMATING LOCAL image PROPERTIES

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computer vision GRAPHICS AND image PROCESSING 1983年第3期21卷 368-382页

作者： BURT, PJ Image Processing Laboratory Electrical Computer and Systems Engineering Department Rensselaer Polytechnic Institute Troy New York 12181

A common task in image analysis is that of measuring image properties within local windows. Often usefulness of these property estimates is determined by characteristics of the windows themselves. Critical factors include the window size and shape, and the contribution the window makes to the cost of computation, A highly efficient procedure for computing property estimates within Gaussian-like windows is described. Estimates are obtained within windows of many sizes simultaneously.

关键词： image PROCESSING

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Convergence analysis of LMS and NLMS adaptive algorithms

Convergence analysis of LMS and NLMS adaptive algorithms

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： T. Hsia Signal and Image Processing Laboratory Department of Electrical and Computer Engineering University of California Davis CA USA

The main contribution of this paper is the unified treatment of convergence analysis for both LMS and NLMS adaptive algorithms. The following new results are obtained: (i) necessary and sufficient conditions of convergence, (ii) optimal adjustment gains and optimal convergence rates, (iii) interrelationship between LMS and NLMS gains, and (iv) non-stationary algorithm design.

关键词： Convergence Algorithm design and analysis Least squares approximation Adaptive algorithm Recursive estimation Adaptive filters Random processes Equations Covariance matrix Signal processing

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DETECTION OF OSTEOGENESIS IMPERFECTA BY AUTOMATED TEXTURE analysis

COMPUTER GRAPHICS AND IMAGE PROCESSING

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computer GRAPHICS AND image PROCESSING 1982年第3期20卷 229-243页

作者： TERZOPOULOS, D ZUCKER, SW Computer Vision and Graphics Laboratory Department of Electrical Engineering McGill University Montreal Quebec Canada

An automated system for detecting Osteogenesis Imperfecta (OI), an inheritable disorder of human connective tissue, is described. The approach is one of texture analysis, founded on standard statistical recognition of co-occurrence-based texture descriptors. Our contribution is to show that texture descriptors derived from gray-level co-occurrence matrices can be used in conjunction with descriptors derived from generalized co-occurrence matrices of local image features to increase performance. In fact, for the OI problem, our system demonstrates a level of performance which is significantly better than that of medical specialists.

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