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15th International Conference on Pattern Recognition, ICPR 2000

作者： Mostafa, Mostafa G.-H. Perkins, Timothy C. Farag, Aly A. Computer Vision and Image Processing Laboratory University of Louisville KY40292 United States

ISBN: (纸本)0769507506

The goal of this paper is twofold. First, we present a supervised fuzzy c-mean (SFCM) classifier for the classification of high dimensional data. Comparisons with conventional FCM clustering technique and Bayesian classification technique are also presented. Second, we present a two-step classifier in which the proposed SFCM and Bayesian algorithms ore used in a cooperative way such that classification results of the SFCM algorithm are used to compute the prior probabilities required for the Bayesian classifier. Classification results of the three algorithms are presented on simulated and real remote sensing multispectral data. The results show improvement in the classification accuracy and reliability using the two-step algorithm. © 2000 IEEE.

关键词： Clustering algorithms

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Multimodality image registration and fusion using neural network

Multimodality image registration and fusion using neural net...

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3rd International Conference on Information Fusion, FUSION 2000

作者： Mostafa, Mostafa G. Farag, Aly A. Essock, Edward Computer Vision and Image Processing Laboratory Department of Electrical and Computer Engineering University of Louisville Louisville KY 40292 United States Department of Psychology University of Louisville Louisville KY 40292 United States

ISBN: (纸本)2725700000

Multi-modality image registration and fusion are essential steps in building 3D models from remote sensing data. In this paper, we present a neural network technique for the registration and fusion of multi-modality remote sensing data for the reconstruction of 3D models of terrain regions. A feedforward neural network is used to fuse the intensity data sets with the spatial data set after learning its geometry. Results on real data are presented. Human performance evaluation is assessed on several perceptual tests in order to evaluate the fusion results. © 2000 Int. Soc. Inf. Fusion.

关键词： image fusion

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A two-step fuzzy-Bayesian classification for high dimensional data

A two-step fuzzy-Bayesian classification for high dimensiona...

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International Conference on Pattern Recognition

作者： M.G.-H. Mostafa T.C. Perkins A.A. Farag Computer Vision and Image Processing Laboratory University of Louisville KY USA

ISBN: (纸本)0769507506

The goal of this paper is twofold. First, we present a supervised fuzzy c-mean (SFCM) classifier for the classification of high dimensional data. Comparisons of the conventional FCM clustering technique and Bayesian classification technique are also presented. Next, we present a two-step classifier in which the proposed SFCM and Bayesian algorithms are used in a cooperative way such that classification results of the SFCM algorithm are used to compute the prior probabilities required for the Bayesian classifier. Classification results of the three algorithms are presented on simulated and real remote sensing multispectral data. The results obtained show improvements in the classification accuracy and reliability using the two-step algorithm.

关键词： Clustering algorithms Bayesian methods Remote sensing Uncertainty High-resolution imaging Hyperspectral sensors Hyperspectral imaging Fuzzy logic image segmentation Classification algorithms

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Supervised fuzzy and Bayesian classification of high dimensional data: a comparative study

Supervised fuzzy and Bayesian classification of high dimensi...

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IEEE International Conference on image processing

作者： M.G.-H. Mostafa T.C. Perkins A.A. Farag Computer Vision and Image Processing Laboratory University of Louisville Louisville KY USA

ISBN: (纸本)0780362977

This paper presents a supervised fuzzy c-mean (SFCM) classifier for the classification of high dimensional data. The proposed SFCM classifier can be iterative or non iterative to reduce the computational time. Comparison with the conventional FCM clustering technique and the Bayesian classification technique is also presented. Performance results of the three algorithms are presented on simulated and real remote sensing multispectral data, which show improvement in the classification accuracy using the SFCM technique.

关键词： Bayesian methods Clustering algorithms Iterative algorithms Remote sensing computer vision Fuzzy logic Pixel Uncertainty Classification algorithms image processing

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Object modeling using space carving

Object modeling using space carving

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IEEE International Conference on image processing

作者： E.E. Hemayed A.A. Farag Computer Vision and Image Processing Laboratory University of Louisville Louisville KY USA

We consider the problem of computing the 3D shape of an unknown, arbitrary-shaped object. Starting with an initial surface, larger than the scene, we refine it using the object's projections taken at known but arbitrarily distributed viewpoints. These projections are used independently in a recursive manner. Thus, using more projections improves the reconstruction with a slight increase in the computation time. The technique generates an approximate voxelized representation of the object. We describe the proposed technique and outline the algorithm. Rendered images of voxel spaces recovered from synthetic and real observation images are shown.

关键词： image reconstruction Layout computer vision image processing Uniform resource locators Shape Rendering (computer graphics) Pipelines Data acquisition image sequences

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CardEye: a 3D trinocular active vision system

CardEye: a 3D trinocular active vision system

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International Conference on Intelligent Transportation

作者： E.E. Hemayed M.T. Ahmed A.A. Farag Computer Vision and Image Processing Laboratory University of Louisville Louisville KY USA

CardEye is an experimental, trinocular, 3D active vision system. Our goal is to create a flexible, precise tool for active vision research. The system uses an agile trinocular vision head mounted on a robotic arm. It has five degrees of freedom: pan, tilt, roll, vergence and variable baseline in addition to the automated zoom and focus of the lenses. It utilizes an active lighting device to assist in the surface reconstruction process. In this paper we describe the architecture of the system together with its functionality.

关键词： Machine vision computer vision Head Focusing Cameras Humans Laboratories Robot vision systems Lenses Surface reconstruction

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Volume registration by surface point signature and mutual information maximization with applications in intra-operative MRI surgeries

Volume registration by surface point signature and mutual in...

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IEEE International Conference on image processing

作者： A.M. Eldeib S.M. Yamany A.A. Farag Computer Vision & Image Processing Laboratory University of Louisville Louisville KY USA Computer Science Old Dominion University VA USA

ISBN: (纸本)0780362977

Intra-operative MRI (iMRI) is a new technology that allows near real time updates of scans during a surgical procedure under the magnet. From an initial comprehensive scan, 3D volumes are obtained. During the surgery, only the portion of the organ under surgery needs to be re-scan (region of interest-ROI) in order to guide the progress of the surgery, and validate the procedure. We describe a registration approach which can be used for iMRI applications. Registration is performed through a hybrid of the surface point signature approach (SPS) and the maximization of the mutual information (MI) criterion. We demonstrate the approach on some real data.

关键词： Mutual information Surges Application software Magnetic resonance imaging Surgery Shape computer vision image processing Laboratories computer science

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Multimodality image registration and fusion using neural network

Multimodality image registration and fusion using neural net...

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International Conference on Information Fusion

作者： M.G. Mostafa A.A. Farag E. Essock Department of Electrical and Computer Engineering Computer Vision and Image Processing Laboratory University of Louisville Louisville KY USA Department of Psychology University of Louisville Louisville KY USA

关键词： image registration Neural networks Hyperspectral sensors Remote sensing image reconstruction Geometry Remote monitoring Intelligent sensors Satellites Laser radar

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Digital topology of sets of convex voxels

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Graphical Models 2000年第5期62卷 343-352页

作者： Saha, Punam K. Rosenfeld, Azriel Medical Image Processing Group University of Pennsylvania Philadelphia PA 19104-6021 United States Computer Vision Laboratory Center for Automation Research University of Maryland College Park MA 20742-3275 United States

Classical digital geometry deals with sets of cubical voxels (or square pixels) that can share faces, edges, or vertices, but basic parts of digital geometry can be generalized to sets S of convex voxels (or pixels) that can have arbitrary intersections. In particular, it can be shown that if each voxel P of S has only finitely many neighbors (voxels of S that intersect P), and if any nonempty intersection of neighbors of P intersects P, then the neighborhood N(P) of every voxel P is simply connected and without cavities, and if the topology of N(P) does not change when P is deleted (i.e., P is a 'simple' voxel), then deletion of P does not change the topology of S.

关键词： Topology

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Multi-modal medical volumes fusion by surface matching 2nd

Multi-modal medical volumes fusion by surface matching

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2nd International Conference on Medical image Computing and computer-Assisted Intervention, MICCAI 1999

作者： Eldeib, Ayman M. Yamany, Sameh M. Farag, Aly A. Computer Vision and Image Processing Laboratory University of Louisville LouisvilleKY40292 United States

ISBN: (纸本)354066503X

This paper presents a fast, six degrees of freedom registration technique to accurately locate the position and orientation of medical volumes (obtained from CT/MRI scans for the same patient) with respect to each other. The main contribution of this work is the use of a novel surface registration technique followed by a volume registration approach. The advantage of this combination is to have an accurate alignment and to reduce the time needed for registration. The surface registration uses a new surface representation scheme that captures the curvature information and codes it into 2-D images. Matching these images enables the recovery of the transformation parameters. For the multi-modal volume registration, the maximization of Mutual Information (MI) is used as a matching criterion and is enhanced by a new genetic based search technique. The results demonstrate that the new two-stage registration technique presented in this paper is robust in terms of speed and accuracy and allows for completely automatic registration of multimodality medical volumes. © Springer-Verlag Berlin Heidelberg 1999.

关键词： Degrees of freedom (mechanics)

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