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1.5V resistive fuse for image smoothing and segmentation

electronicS LETTERS 1997年第10期33卷 851-852页

作者： Athreya, JKV Gregson, PH Department of Electrical Engineering Computer Vision and Image Processing Laboratory Technical University of Nova Scotia Halifax Canada

A 1.5V resistive fuse for image smoothing and segmentation using bulk-driven MOSFETs is presented. The circuit switches on only if the differential voltage applied across its input terminals is less than a set voltage;it switches off if the differential voltage is higher than the set value. The useful operation range of the circuit is 0.4V with a supply voltage of 1.5V and threshold voltages of V-Tn = 0.828V and V-Tp = -0.56V for n and g channel MOSFETs, respectively.

关键词： image processing segmentation (image processing)

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Two-stage neural network for volume segmentation of medical images

Two-stage neural network for volume segmentation of medical ...

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1997 IEEE International Conference on Neural Networks (ICNN 97)

作者： Ahmed, MN Farag, AA Computer Vision and Image Processing Laboratory University of Louisville Department of Electrical Engineering Louisville KY 40292 United States

ISBN: (纸本)0780341236

In this paper, we present a new system to segment and label CT/MRI Brain slices using feature extraction and unsupervised clustering. In this technique, each voxel is assigned a feature pattern consisting of a scaled family of differential geometrical invariant features. The invariant feature pattern is then assigned to a specific region using a two-stage neural network system. The first stage is a self-organizing principal components analysis (SOPCA) network that is used to project the feature vector onto its leading principal axes found by using principal components analysis. This step provides an effective basis for feature extraction. The second stage consists of a self-organizing feature map (SOFM) which will automatically cluster the input vector into different regions. The optimum number of regions (clusters) is obtained by a model fitting approach. Finally, a 3D connected component labeling algorithm is applied to ensure region connectivity. Implementation and performance of this technique are presented. Compared to other approaches, the new system is more accurate in extracting 3D anatomical structures of the brain, and can be apdated to real-time imaging scenarios.

关键词： segmentation pattern analysis learning in vision

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3D reconstruction of the human jaw from a sequence of images

3D reconstruction of the human jaw from a sequence of images

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Conference on computer vision and Pattern Recognition (CVPR)

作者： M. Ahmed S. Yamany E. Hemayed S. Ahmed S. Roberts A. Farag Computer Vision and Image Processing Laboratory Department of Electrical Engineering University of Louisville Louisville KY USA

A novel approach is proposed to obtain a record of the patient's occlusion using computer vision. Data acquisition is obtained using intra-oral video cameras. The technique utilizes shape from shading to extract 3D information from 2D views of the jaw, and a novel technique for 3D data registration using genetic algorithms. The resulting 3D model can be used for diagnosis, treatment planning, and implant purposes. The overall purpose of this research is to develop a model-based vision system for orthodontics to replace traditional approaches. This system will be flexible, accurate, and will reduce the cost of orthodontic treatments.

关键词： image reconstruction Humans computer vision Data acquisition Cameras Shape Data mining Genetic algorithms Implants Machine vision

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Two-stage neural network for volume segmentation of medical images

Two-stage neural network for volume segmentation of medical ...

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International Conference on Neural Networks

作者： M.N. Ahmed A.A. Farag Computer Vision and Image Processing Laboratory Department of Electrical Engineering University of Louisville Louisville KY USA

We present a system to segment and label CT/MRI brain slices using feature extraction and unsupervised clustering. In this technique, each voxel is assigned a feature pattern consisting of a scaled family of differential geometrical invariant features. The invariant feature pattern is then assigned to a specific region using a two-stage neural network system. The first stage is a self-organizing principal components analysis (SOPCA) network that is used to project the feature vector onto its leading principal axes found by using principal components analysis. This step provides an effective basis for feature extraction. The second stage consists of a self-organizing feature map (SOFM) which will automatically cluster the input vector into different regions. The optimum number of regions (clusters) is obtained by a model fitting approach. Finally, a 3D connected component labeling algorithm is applied to ensure region connectivity. Implementation and performance of this technique are presented. Compared to other approaches, the new system is more accurate in extracting 3D anatomical structures of the brain, and can be adapted to real-time imaging scenarios.

关键词： Neural networks image segmentation Biomedical imaging Feature extraction Principal component analysis Computed tomography Magnetic resonance imaging Brain Labeling Clustering algorithms

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3D segmentation and labeling using self-organizing Kohonen network for volumetric measurements on brain CT imaging to quantify TBI recovery

3D segmentation and labeling using self-organizing Kohonen n...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： M.N. Ahmed A.A. Farag Computer Vision and Image Processing Laboratory Department of Electrical Engineering University of Louisville Louisville KY USA

In this paper, we present a new system to segment and label CT brain slices using a self-organizing Kohonen network. Our aim is to extract reliable and robust measures from CT images of Traumatic Brain Injury (TBI) patients that can accurately describe the morphological changes in the brain as recovery progresses. Segmentation is performed by assigning a feature pattern to each voxel, consisting of a scaled family of differential geometrical invariant features. The invariant feature pattern is input to Kohonen network for an unsupervised classification of the voxels into regions.

关键词： Labeling Volume measurement image segmentation Computed tomography Layout Electric variables measurement engineering in Medicine and Biology Society Brain injuries Data mining Filters

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Measuring the distance and orientation of a planar surface using nonstructured lighting

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SYSTEMS AND computerS IN JAPAN 1996年第8期27卷 83-92页

作者： Tsukiyama, T Member Electrotechnical Laboratory Tsukuba Japan 305 Tosbifumi Tsukiyama:graduated in 1972 from the Biological Engineering Department Osaka University where he also received his Dr. of Eng. degree. He joined Electrotechnical Laboratory Ministry of Tread and Industry (Japan) in 1972. At present he is Senior Researcher at Computer Vision Section Intelligent Systems Division ETL. He was a Visiting Researcher at University of Illinois during 1984–1985. He has been interested in vision system for mobile robots and image processing architectures. He is a member of the Society of Instrumental and Control Engineers Japan Robotics Society of Japan and Information Processing Society of Japan.

This paper proposes a method of 3-dimensional measurement of a planar surface by using two fixed light sources and a TV camera. A set of two images is recorded by the camera switching on each light alternately. The peak of the luminance distribution in each image is detected, and the orientation and distance of the planar surface are calculated. The position of the peak of a luminance distribution can be estimated accurately by using an image processing. The light sources can be conventional apparatus with no particular structure. The method is simple and suitable for a vision system on an indoor mobile robot.

关键词： distance to surface orientation of planar surface point light source luminance distribution ring light

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Texture segmentation using hierarchical wavelet decomposition

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PATTERN RECOGNITION 1995年第12期28卷 1819-1824页

作者： Salari, E Ling, Z Computer Vision and Image Processing Laboratory Department of Electrical Engineering University of Toledo Toledo OH 43606 U.S.A.

This paper presents a texture segmentation algorithm based on a hierarchical wavelet decomposition. Using Daubechies' four-tap filter, an original image is decomposed into three detail images and one approximate image. The decomposition can be recursively applied to the approximate image to generate a lower resolution of the pyramid. The segmentation starts at the lowest resolution using the K-means clustering scheme and textural features obtained from various sub-bands. The result of segmentation is propagated through the pyramid to a higher resolution with continuously improving the segmentation. The lower resolution levels help to build the contour of the segmented texture, while higher levels refine the process, and correct possible errors.

关键词： Texture segmentation Wavelet transform Clustering algorithm Multiresolution segmentation

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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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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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