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International Conference on Information Fusion

作者： A.E. Abdel-Hakim A.A. Farag Computer Vision and Image Processing Laboratory CVIP Lab University of Louisville Louisville KY USA

In this paper, we present a novel and robust model for camera planning in smart vision systems. The proposed approach uses virtual forces to adjust camera parameters (pan and tilt) to the most proper values with respect to the application. The proposed model employs the information in the acquired image and some of the intrinsic camera parameters to estimate pan and tilt displacements required to bring a target object into a specific location of interest in the image. Our model is a general framework and any vision system can be easily modeled to use it. This approach has several advantages over previous work in camera planning. It is portable, expandable, robust, and flexible. Also, there is no need for complicated calibration for the cameras or their pan-tilt heads. The results show that our approach is efficient even with poor system initialization and it is robust against possible weakness in the auxiliary algorithms used.

关键词： Robustness Cameras Machine vision Calibration Process planning Head Lenses Focusing Surveillance computer vision

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Color segmentation using an Eigen color representation

Color segmentation using an Eigen color representation

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International Conference on Information Fusion

作者： A.E. Abdel-Hakim A.A. Farag Computer Vision and Image Processing Laboratory CVIP Laboratory University of Louisville Louisville KY USA

In real images, color segmentation is a challenging problem because of natural variations in light and shadows. Most existing color models have limitations in distinctive representation of colors in real scenes. Color segmentation approaches are highly dependent on the robustness of the used color model to these variations. In this paper, we present a novel color representation for color segmentation. The proposed model is a fusion of classical color representations. It is calculated using a closed form linear transformation from a high dimensional classical feature space to a lower dimensional new space. We prove the high performance of the proposed model in terms of robustness to natural variations and distinction between different colors, when compared with classical color models. Through color segmentation of real images, we provide a quantitative and qualitative comparison between the proposed model and classical models. The experimental results support the potential of the proposed model.

关键词： image segmentation Robustness computer vision image processing Layout Color Laboratories Application software computer graphics Digital images

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Shape-constraint for accurate segmentation in remote sensing imagery

Shape-constraint for accurate segmentation in remote sensing...

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International Conference on Information Fusion

作者： A. El-Baz R. Mohamed A. Farag Computer Vision and Image Processing Laboratory (CVIP Lab) University of Louisville Louisville KY USA

A new approach is proposed for the segmentation of remote sensing images which is based on using a prior shape information. This information is obtained from a set of signed distance functions (SDF). Each SDF represents the training data set as a histogram of the occurrences of the data points inside and outside a specific object. A statistical approach is proposed for modeling each SDF, which is considered to have a random distribution. The mean field theory-based support vector machines (MF-based SVM) density estimator is used to estimate the probability density function which underlies the random distribution of the SDF. The statistical modeling of shapes enables realistic representation of objects especially those which are not contiguous within the image; i.e., objects which consist of multiple regions. This modeling is used to substitute for the class priors in a Bayes segmentation setup, in which the class conditional probabilities are estimated using the MF-based SVM algorithm also. Experiments with multispectral remote sensing data show that the proposed segmentation approach is more accurate and robust than other known alternatives.

关键词： image segmentation Remote sensing Shape measurement Surface cleaning Support vector machines computer vision Pixel image processing Laboratories Training data

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Remote sensing image segmentation using SVM with automatic selection for the kernel parameters

Remote sensing image segmentation using SVM with automatic s...

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International Conference on Information Fusion

作者： R. Mohamed A. El-Baz A. Farag Computer Vision and Image Processing Laboratory (CVIP Lab) University of Louisville Louisville KY USA

The kernel function plays a basic role in support vector machines (SVM) algorithms. This paper presents an automatic method for selecting the parameters of the Gaussian radial basis function (GRBF) kernel which is one of the commonly used forms in SVM regression algorithms. The proposed method uses the expectation maximization (EM) algorithm for the automatic selection. The SVM regression algorithm is used to solve two major problems in remote sensing image segmentation: the density estimation problem and the Markov random field modeling problem. The density estimation is used for class conditional probabilities in Bayesian setups. The MRF is used for region modeling in boosting the Bayesian image segmentation. The proposed algorithms are integrated in a framework for remote sensing image segmentation. Experimental evaluation of the proposed algorithms using synthetic data set and hyperspectral data set illustrates the outstanding performance of the proposed algorithms.

关键词： Remote sensing image segmentation Support vector machines Kernel Probability Markov random fields Bayesian methods Covariance matrix computer vision image processing

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A robust matching algorithm for active appearance models 1

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Workshops Bildverarbeitung fur die Medizin: Algorithmen - Systeme - Anwendungen, BVM 2005 - Workshop on image processing for Medicine: Algorithms - Systems - Applications, BVM 2005

作者： Beichel, Reinhard Bischof, Horst Langs, Georg Sonka, Milan Institute for Computer Graphics and Vision Graz University of Technology Inffeldgasse 16/2 A-8010 Graz Austria Pattern Recognition and Image Processing Group Vienna University of Technology Favoritenstr. 9 A-1040 Vienna Austria Department of Electrical and Computer Engineering University of Iowa Iowa City IA 52242 United States

ISBN: (数字)9783540264316

ISBN: (纸本)3540250522

This paper proposes a method for robustly matching active appearance models (AAMs) on images with gross disturbances (outliers). The method consists of two steps. First, an initial residual is calculated by comparing model and image appearance, and modes of the residual are analyzed. Second, all possible mode combinations are tested by evaluating an objective function. The objective function allows the selection of an outlier-free mode combination. Experiments demonstrate the ability of the robust matching method to successfully cope with outliers - compared to standard AAM matching, no degeneration of the model during matching occurs.

关键词： Statistics

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Variational-based method to extract parametric shapes from images

Variational-based method to extract parametric shapes from i...

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International Conference on computer vision (ICCV)

作者： M.T. El-Melegy N.H. Al-Ashwal A.A. Farag Electrical Engineering Department Assiut University Asyut Egypt Computer Vision and Image Processing Laboratory University of Louisville KY USA

In this paper, we propose a variational method to segment image objects, which have a given parametric shape based on a level-set formulation of the Mumford-Shah functional, and the shape parameters. We define an energy functional composed by two complementary terms. The first one detects object boundaries using a Chan-Vese-like method. The second term constrains the contour to find a shape compatible with the parametric shape. The segmentation of the object of interest is given by the minimum of our energy functional. This minimum is computed with the calculus of variation and the gradient descent method that provide a system of evolution equations solved with the well-known level set method. We focus in this paper on the parametric category of image linear objects. Applications of the proposed model are presented on synthetic and real images

关键词： Shape image segmentation Level set computer vision Focusing image processing Object detection Calculus Application software Equations

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Optimal sub-shape models by minimum description length

Optimal sub-shape models by minimum description length

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Conference on computer vision and Pattern Recognition (CVPR)

作者： G. Langs P. Peloschek H. Bischof Institute for Computer Graphics and Vision Graz University of Technology Graz Austria Pattern Recognition and Image Processing Group University of Technology Vienna Vienna Austria Department of Clinical Radiology Vienna Medical University Vienna Austria

Active shape models are powerful and widely used tool to interpret complex image data. By building models of shape variation they enable search algorithms to use a priori knowledge in an efficient and gainful way. However, due to the linearity of PCA, non-linearities like rotations or independently moving sub-parts in the data can deteriorate the resulting model considerably. Although non-linear extensions of active shape models have been proposed and application specific solutions have been used, they still need a certain amount of user interaction during model building. In this paper the task of building/choosing optimal models is tackled in a more generic information theoretic fashion. In particular, we propose an algorithm based on the minimum description length principle to find an optimal subdivision of the data into sub-parts, each adequate for linear modeling. This results in an overall more compact model configuration. Which in turn leads to a better model in terms of modes of variations. The proposed method is evaluated on synthetic data, medical images and hand contours.

关键词： Active shape model Biomedical imaging Principal component analysis computer vision Linearity Polynomials Pattern recognition computer graphics Radiology image processing

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Novel vibro-acoustical technology for pipe deposition monitoring

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International Journal of COMADEM 2005年第4期8卷 24-32页

作者： Gelman, L. Jenkin, P. Petrunin, I. Sanderson, M. Thompson, C. Cranfield University National Technical University of Ukraine Cranfield University Department of Process and Systems Engineering Applied Mathematics and Computing Group Cranfield Beds MK43 0AL United Kingdom Applied Mathematics and Computing Group Cranfield University National Technical University of Ukraine KPI Department of Process and Systems Engineering Department of Fluid Instrumentation School of Engineering Cranfield University Centre for Computer Vision and Image Processing

Deposits (waxes, hydrates, etc.) change the damping properties of pipelines. The damping ratio of the pipeline increases with increments in deposit thickness. Thus, the evaluation of the damping ratio of the pipeline provides a condition monitoring of the pipeline deposits. Deposits could, be detected by monitoring the vibro-acoustical response of the pipeline while it is subjected to vibration excitation. Vibro-acoustical pipeline condition monitoring is a promising technology, but it has been little investigated. A novel vibro-acoustical technology of deposit detection in pipelines is proposed, developed, and validated through simulation. A new generic feature representation approach is used. Comparison of the proposed technology with the traditional technology based on the magnitude of the frequency response function is undertaken using simulation (by the Simulink) and Monte Carlo procedure. It is shown that the proposed technology provides an essential monitoring effectiveness gain vs. that of the technology based on the magnitude of the frequency response function. Thus, the proposed technology significantly improves the accuracy of vibro-acoustical deposit monitoring in pipelines. © 2005 COMADEM International.

关键词： Condition monitoring

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image content-based active sensor planning for a mobile trinocular active vision system

Image content-based active sensor planning for a mobile trin...

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2004 International Conference on image processing, ICIP 2004

作者： Farag, Aly A. Abdel-Hakim, Alaa E. Computer Vision and Image Processing Laboratory University of Louisville Louisville KY 40292

In this paper, we present a sensor planning approach for a mobile trinocular active vision system. At the stationary state (i.e., no motion) the sensor planning system calculates the generalized cameras' parameters (i.e., translational distance from the center, zoom, focus and vergence) using deterministic geometric specifications of both the sensors and the objects in their field of view. Some of these geometric parameters are difficult to be predetermined for the mobile system operation. In this paper, a new sensor planning approach, based on processing the content of the captured images, is proposed. The approach uses a combination of a closed-form solution for the translation between the three cameras, the vergence angle of the cameras as well as zoom and focus settings with the results of the correspondences between the acquired images and a predefined target object(s) obtained using the SIFT algorithm. We demonstrate the accuracy of the new approach using practical experiments. © 2004 IEEE.

关键词： image processing

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Shape-based MRI data segmentation in the presence of intensity inhomogeneities using level sets

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International Congress Series 2004年第C期1268卷 213-218页

作者： Hassan, Hossam Farag, Aly A. Electrical and Computer Engineering Computer Vision and Image Processing Laboratory University of Louisville Louisville KY 40292 403 Lutz Hall Building Universit United States

Magnetic resonance imaging (MRI) intensity inhomogeneities can be attributed to imperfections in the RF coils or some problems associated with the acquisition sequences. The result is a slowly varying shading artifact over the image that can produce errors with conventional intensity-based classification. The removal of the spatial intensity inhomogeneities from MR images is difficult because the inhomogeneities could change with different MRI acquisition parameters from patient to patient and from slice to slice. We propose a shape-based classification technique to overcome this problem using the level set approach. A signed distance function is dedicated to describe the model of the tissue of interest. Then, a partial differential equation (PDE) is derived to describe the evolution of an observed structure. At each iteration, the observed structure is registered with the prior shape model. The algorithm is used at different levels of noise and intensity inhomogeneities showing a good accuracy. © 2004, Elsevier Inc. All rights reserved.

关键词： Bias field image segmentation Level set segmentation MR imaging PDE

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