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1993 IEEE Workshop on Qualitative vision, WQV 1993

作者： Fermüller, Cornelia Computer Vision Laboratory Center for Automation Research University of Maryland College ParkMD20742-3275 United States Department for Pattern Recognition and Image Processing Institute for Automation Technical University Vienna Treitlstraße 3 ViennaA-1040 Austria

ISBN: (纸本)0818636920

The problem of egomotion recovery has been treated by using as input local image motion, with the published algorithms utilizing the geometric constraint relating 2-D local image motion (optical flow, correspondence, derivatives of the image flow) to 3-D motion and structure. Since it has proved very difficult to achieve accurate input (local image motion), a lot of effort has been devoted to the development of robust techniques. A new approach to the problem of egomotion estimation is taken, based on constraints of a global nature. It is proved that local normal flow measurements form global patterns in the image plane. The position of these patterns is related to the three dimensional motion parameters. By locating some of these patterns, which depend only on subsets of the motion parameters, through a simple search technique, the 3-D motion parameters can be found. The proposed algorithmic procedure is very robust, since it is not affected by small perturbations in the normal flow measurements. As a matter of fact, since only the sign of the normal flow measurement is employed, the direction of translation and the axis of rotation can be estimated with up to 100% error in the image measurements. © 1993 IEEE.

关键词： Flow measurement

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Local estimation of Gaussian-based edge enhancement filters using Fourier analysis

Local estimation of Gaussian-based edge enhancement filters ...

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

作者： B. Wang D.M. Rose A.A. Farag E.J. Delp Engineering Mathematics & Computer Science Department Speed Scientific School University of Louisville Louisville KY USA Computer Vision & Image Processing Laboratory School of Electrical Engineering Purdue University West Lafayette IN USA

An empirical measure for the selection of the edge-enhancement Gaussian filter is developed. The Gaussian filter is specified by its standard deviation sigma ; the filter's spatial support is a function of sigma . An estimation procedure for sigma using Fourier analysis is described. The measure is easy to implement and is based totally on the image at hand. Experimental results suggest that this measure can be used as an aid in deciding the Gaussian filter's spatial support, which is needed to enhance the edges. Other equivalent bandwidth definitions can be used to obtain a measure of the frequency spread in the smoothed image (e.g., the mean-square bandwidth).< >

关键词： Gaussian processes Low pass filters Shape Filtering Frequency Mathematics computer science computer vision image processing image segmentation

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Motion constraint patterns

Motion constraint patterns

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IEEE Workshop on Qualitative vision

作者： C. Fermuller Department for Pattern Recognition and Image Processing Institute for Automation Technical University of of Vienna Vienna Austria Computer Vision Laboratory Center for Automation Research University of Maryland College Park MD USA

关键词： Motion estimation computer vision Automation image motion analysis Fluid flow measurement Motion measurement Rotation measurement Laboratories Educational institutions Geometrical optics

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A STUDY OF THE GENERALIZED MORPHOLOGICAL FILTER

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CIRCUITS SYSTEMS AND SIGNAL processing 1992年第1期11卷 229-252页

作者： SONG, JS DELP, EJ 1. Computer Vision and Image Processing Laboratory School of Electrical Engineering Purdue University 47907 West Lafayette Indiana USA

A new class of morphological filters is proposed for image enhancement. The filter, known as the generalized morphological filter (GMF), uses multiple structuring elements and combines linear and morphological operations. The GMF can be designed to suppress various types of noise yet preserve geometrical structure in an image. A study of several aspects of the performance of the filter is presented. The study includes geometrical feature preservation, noise suppression, structuring element selection, and the root signal structure. For the sake of comparison, averaging and median filters are also used in the experiments and corresponding figures of merit of the performance of the filter. The empirical study shows that the generalized morphological filter possesses effective noise suppression with reduced geometrical feature blurring.

关键词： Signal Filtering And Prediction

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On empirical estimation of the parameters of edge enhancement filters

On empirical estimation of the parameters of edge enhancemen...

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IEEE International Conference on Systems, Man and Cybernetics

作者： A.A. Farag Y. Cao D.M. Rose E.J. Delp Engineering Mathematics & Computer Science Department Speed Scientific School University of Louisville Louisville KY USA Computer Vision & Image Processing Laboratory School of Electrical Engineering Purdue University West Lafayette IN USA

The authors develop an empirical measure for the selection of the Gaussian filter that is commonly used for edge enhancement. The measure is based totally on the image at hand. Edge enhancement by a Gaussian filter has two distinct advantages: (1) the filter is fully described by a single parameter, the standard deviation sigma ; (2) the two-dimensional filter is separable and can be easily implemented. The filter's spatial support is a function of sigma . This support is normally in the range of +or-3.5 sigma . An empirical measure is described for the selection of the Gaussian filter's spatial support using the power spectrum density of the input image. Classic Fourier analysis is used to obtain a measure for the spatial support of the Gaussian filter given a particular image. Experimental results suggest that this measure can be used as an aid in deciding the Gaussian filter's spatial support needed to enhance the edges.< >

关键词： Parameter estimation Low pass filters image edge detection image segmentation Joining processes Smoothing methods Frequency estimation Density functional theory Shape Mathematics

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Automatic neural arbor reconstruction

Automatic neural arbor reconstruction

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： P.M. Gregson Computer Vision and Image Processing Laboratory Department of Electrical Engineering Technical University of Nova Scotia Halifax NS Canada

An algorithm for detecting neural processes in serial optical sections for use in an automated three-dimensional neural reconstruction system is presented. This parsimonious, nonlinear, psychophysically motivated algorithm addresses the problems specific to neural element detection and localization, viz., images with minimal resolution, operators with small spatial supports, highly curved, filamentous features, large variation in feature intensity profile, poor signal-to-noise ratio, and determination of depth without stereo. One first finds the magnitude and orientation of the maximum intensity second directional derivative. A family of curves is locally fitted to these data, and the projections of the data on the curve family are found. If a pixel lies on a curve with sufficient total projection, it is labeled with the magnitude, orientation, curvature, spatial extent, and element displacement. Depth is interpolated from the spatial extent data for corresponding neighborhoods in three adjacent (in depth) images by using an approximation to the depth-dependent optical point spread function. Experimental results using photomicrographs of cat visual cortex are presented.< >

关键词： image reconstruction computer vision Displays Spatial resolution Neural networks Nonlinear optics Optical interconnections Neurons image processing Laboratories

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A path metric for sequential search and its application in edge linking

A path metric for sequential search and its application in e...

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IEEE International Conference on Systems, Man and Cybernetics

作者： A.A. Farag E.J. Delp Engineering Mathematics & Computer Science Department Speed Scientific School University of Louisville Louisville KY USA Computer Vision & Image Processing Laboratory School of Electrical Engineering Purdue University West Lafayette IN USA

The authors develop a path metric for sequential search based on the linear model. The metric forms the heart of an edge-linking algorithm that combines edge elements enhanced by an optimal filter. From a starting node, transitions are made to the goal nodes by a maximum likelihood metric. This metric requires only local calculations on the search space and its use in edge linking provides more accurate results than other linking techniques.< >

关键词： Joining processes image edge detection Filters Application software Heart Layout Decoding Mathematics computer science computer vision

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INVARIANT RECOVERY OF CURVES IN M-DIMENSIONAL SPACE FROM SPARSE DATA

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JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS image SCIENCE AND vision 1990年第3期7卷 480-490页

作者： STEVENSON, RL DELP, EJ Computer Vision and Image Processing Laboratory School of Electrical Engineering Purdue University West Lafayette Indiana 47907 USA

We address the problem of reconstructing a smooth curve from sparse and noisy information that is invariant to the choice of the coordinate system. Tikhonov regularization is used to form a well-posed mathematical problem statement, and conditions for an invariant reconstruction are given. The resulting functional minimization problem is shown to be nonconvex. Approximations to the invariant functional are often used to form a convex problem that can be solved efficiently. Two common approximations, those of cubic and weighted cubic splines, are detailed, and examples are given to show that the approximations are often invalid. To form a valid approximation to the invariant functional we propose a two-step algorithm. The first step forms a piecewise-linear curve, which is invariant to the coordinate system. This piecewise-linear curve is then used to construct a parameterization of the curve for which we can make a valid approximation to the invariant functional. Examples are given to demonstrate the effectiveness of the algorithm, and two example applications for which the invariant property is important are given.

关键词： Algorithms Curvature Inverse problems Machine vision Reconstruction algorithms Strain

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A TECHNIQUE FOR THE VISUALIZATION AND ANALYSIS OF CARDIAC WALL MOTION BY TWO-DIMENSIONAL ECHOCARDIOGRAPHY

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IEEE TRANSACTIONS ON MEDICAL IMAGING 1989年第1期8卷 104-106页

作者： MELENDO, EG DELP, EJ Computer Vision and Image Processing Laboratory School of Electrical Engineering Purdue University West Lafayette IN USA

A technique is presented that can be used in the visualization and analysis of cardiac wall motion abnormalities by digital two-dimensional echocardiography. This technique is based on the use of a curvature function extracted from the endocardial boundary locations and can be used for shape or shape change analysis of the heart. The authors call the locations of high absolute curvature landmarks. Identification of landmarks on the endocardial boundary provides a simplified but powerful description of the boundary that allows visualization and analysis of wall motion. The simplification of the heart beating process is an excellent tool for the identification of infarcted areas of the heart.

关键词： Visualization Endocardium curvature function Cardiac wall motion 2D Echocardiography landmarks

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The use of mathematical morphology in image enhancement

The use of mathematical morphology in image enhancement

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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： J. Song R.L. Stevenson E.J. Delp Computer Vision and Image Processing Laboratory Purdue University West Lafayette IN USA

The structure and properties of several morphological filtering algorithms are discussed. The performance of the algorithms when applied to images corrupted with noise is presented. Several open research problems rela... 详细信息

关键词： Morphology image enhancement Nonlinear filters Filtering algorithms image processing Morphological operations Gray-scale Information filtering Information filters computer vision

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