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Differential methods for nonmetric calibration of camera lens distortion

Differential methods for nonmetric calibration of camera len...

引用

Conference on computer vision and Pattern Recognition (CVPR)

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

ISBN: (纸本)0769512720

This paper addresses the problem of calibrating camera lens distortion, which can be significant in medium to wide angle lenses. Our approach is based on the analysis of distorted images of straight lines. We derive new distortion measures that can be optimized using non-linear search techniques to find the best distortion parameters that straighten these lines. Unlike other approaches, we also provide fast, closed-form solutions to the distortion coefficients. Experiments to evaluate the performance of this approach on synthetic and real data are reported.

关键词： Calibration Cameras Lenses Nonlinear distortion Distortion measurement Layout Parameter estimation computer vision image processing Laboratories

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Virtual Forces for Camera Planning in Smart vision Systems

Virtual Forces for Camera Planning in Smart Vision Systems

引用

IEEE Workshop on Applications of computer vision (WACV)

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

In this paper, we present a novel and robust approach for camera planning in smart vision systems. The proposed approach uses virtual forces to adjust the camera parameters (pan, tilt, translation ... etc.) toward the most proper values with respect to the application. It employs the physical spring model to direct the motion of the camera toward its target. Our approach 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. We present a case study of a two degree-of-freedom stereo vision system to test our approach. The results show the efficiency of our approach even with poor system initialization and its robustness against possible weakness in the auxiliary algorithms used.

关键词： Smart cameras Machine vision Process planning Robustness Sensor systems Sensor phenomena and characterization Application software computer vision Springs System testing

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Two Sequential Stages Classifier for Multispectral Data

Two Sequential Stages Classifier for Multispectral Data

引用

IEEE computer Society Conference on computer vision and Pattern Recognition Workshops (CVPRW)

作者： Refaat M Mohamed Aly A Farag Computer Vision and Image Processing Laboratory University of Louisville Louisville KY USA

In this paper, we present an approach for the classification of remote sensing multispectral data, which consists of two sequential stages. The first stage exploits the capabilities of the Support Vector Machines (SVM) approach for density estimation and uses it in a Bayes classification setup. In a typical image, the class of a pixel is highly dependent on the classes of its neighbor pixels. The second stage exploits the dependency of the classes. We incorporate this dependency using stochastic modeling of the context as a Markov Random Field (MRF). The MRF is modeled using Besag model and implemented using the Iterative Conditional Modes (ICM) algorithm. Results show that the stochastic modeling approach enhances the results and provides reasonable smoothness in the classified image.

关键词： Support vector machines Estimation Classification algorithms Pixel Density functionional theory Kernel computer vision

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image segmentation using human visual system properties with applications in image compression

Image segmentation using human visual system properties with...

引用

Human vision, Visual processing, and Digital Display 1989

作者： Peterson, Heidi A. Rajala, Sarah A. Delp, Edward J. Computer Vision and Image Processing Laboratory School of Electrical Engineering Purdue University West LafayetteIN47907 United States Department of Electrical and Computer Engineering North Carolina State University RaleighNC27695 United States

Many image compression techniques involve segmentation of a gray level image. With such techniques, information is extracted that describes the regions in the segmented image, and this information is then used to form a coded version of the image. In this paper we present a region-growing-based segmentation technique that incorporates human visual system properties, and describe the use of this technique in image compression. We also discuss the effect of requantizing a segmented image. Requantization of a segmented image is useful because it can lead to a reduction in the number of bits required to code the description of the regions in the segmented image. This results in a lower data rate. We show that the number of gray levels in a segmented image can be reduced by a factor of at least twelve, without noticeable degradation in the quality of the segmented image. This result is attributable to human visual system properties having to do with contrast sensitivity, and to the fact that requantization of a segmented image does not usually reduce significantly the number of distinct segments in the image. In addition, in this paper we explore the relationship between the number of segments in an image, and the extent of requantization possible before noticeable degradation occurs in the image. Finally, we discuss the impact of the above results on image compression algorithms, and present some experimental results. © 1989 SPIE. All rights reserved.

关键词： image compression

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Clinically desired segmentation method for vertebral bodies

Clinically desired segmentation method for vertebral bodies

引用

IEEE International Symposium on Biomedical Imaging

作者： Melih S. Aslan Ahmed Shalaby Aly A. Farag Computer Vision and Image Processing Laboratory University of Louisville Louisville KY USA

ISBN: (纸本)9781467364560

In this paper, we propose a clinically desired segmentation method for vertebral bodies (VBs) in computed tomography (CT) images. Three pieces of information (intensity, spatial interaction, and shape) are modeled to optimize a new probabilistic energy functional; and hence to obtain the optimum segmentation. The information of the intensity and spatial interaction are modeled using the Gaussian and Gibbs distribution, respectively. A shape model is proposed using a new probabilistic function to enhance the segmentation results. This model is a generic shape information which is obtained using the cervical, lumbar, and thoracic spinal regions. We propose a semiautomated segmentation algorithm which uses limited interventions only in the VB separation process. The overall segmentation process completes the task in very low execution time which is one of the most important contribution of this paper. The proposed method is validated with clinical CT images and on a phantom with various Gaussian noise levels. This study reveals that the proposed method is robust under various noise levels, less variant to the initialization, and quite faster than alternative methods. One of the most important contributions of our paper is to offer a segmentation framework which can be suitable to the clinical works with acceptable results.

关键词： image segmentation Shape Computed tomography Probabilistic logic Object segmentation Level set Training

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PDE-based robust robotic navigation

PDE-based robust robotic navigation

引用

Canadian Conference on computer and Robot vision, CRV

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

In this paper, we propose a general, robust, and fast path planning framework for robotic navigation using level set methods. A medial point of the map is selected automatically to be a point source that transmits two wave fronts of different speeds. The first front propagates with a moderate speed to capture the map topology, while the second one propagates much faster at medial points such that the safest paths intersect the propagating fronts at those points of maximum positive curvature, which are identified by solving an ordinary differential equation (ODE). The motion of the front is governed by a nonlinear partial differential equation (PDE), which is efficiently solved using robust level set methods. The framework is general in the sense that it can be used for both 2D and 3D environments. It generates a collision-free optimum paths for partial or complete configuration space. Optimum planned paths can be controlled according to the surrounding environment and running conditions in order to follow the safest, shortest, or hybrid paths. We demonstrate the robustness of the proposed method by correctly extracting planned paths of complex maps with several obstacles.

关键词： Robustness Navigation Path planning Orbital robotics Robot vision systems Level set computer vision Topology image processing Laboratories

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image segmentation using GMRF models: parameters estimation and applications

Image segmentation using GMRF models: parameters estimation ...

引用

IEEE International Conference on image processing

作者： A. El-Baz A.A. Farag Computer Vision and Image Processing Laboratory University of louisville Louisville KY USA

Stochastic models of images are commonly represented in terms of three random processes (random fields) defined on the region of support of the image. The observed image process G is considered as a composite of two random process: a high level process G/sup h/, which represents the regions (or classes) that form the observed image; and a low level process G/sup l/, which describes the statistical characteristics of each region (or class). The representation G = (G/sup h/, G/sup l/) has been widely used in the image processing literature in the past two decades. In this paper, we consider the low level process G/sup l/ as mixture of normal distributions, and we use the expectation-maximization (EM) algorithm to estimate the mean, the variance, and proportion for each distribution. A popular model for the high level process G/sup h/ has been the Gibbs-Markov random field (GMRF) model. We introduce a novel unsupervised approach to estimate the parameters of a GMRF model. In this approach, we estimate the model parameters that maximize the posteriori probability of each pixel in a given image. The MAP estimate is obtained using a combination of genetic search and deterministic optimization using the iterated conditional mode (ICM) approach of Besag. The desired estimate of the GMRF parameters is the one corresponding to the MAP estimate. The approach has been applied on real images (Spiral CT slices) and provides satisfactory results.

关键词： image segmentation Parameter estimation Random processes Stochastic processes image processing Gaussian distribution Pixel Genetics Spirals Computed tomography

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A Global-to-Local 2D Shape Registration in Implicit Spaces using Level Sets

A Global-to-Local 2D Shape Registration in Implicit Spaces u...

引用

IEEE International Conference on image processing

作者： Rachid Fahmi Aly A. Farag Computer Vision & Image Processing laboratory University of Louisville Louisville KY USA

This work deals with the problem of shape registration. Broadly speaking, the problem is that of establishing point-wise correspondences in two different shapes of arbitrary dimension and topology. This problem is a fundamental component in numerous image and vision applications. We propose a variational framework for a dense global-to-local 2D shape registration. Affine transformations are accounted for using vector distance functions. Based on this representation, a dissimilarity measure between the two shapes is minimized to recover the global matching parameters. The local coordinate transformation between the two shapes is explicitly estimated by solving a regularized non-linear PDE-based motion model. Various experimental results are presented and discussed to show the potential of the proposed framework with a finite element (FE)-based validation of its performance.

关键词： Level set Shape measurement computer vision Active contours Mathematical model Anisotropic magnetoresistance image segmentation Kernel Constraint theory Elasticity

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Less is more: Cropping to improve facial recognition with thermal images

Less is more: Cropping to improve facial recognition with th...

引用

International Conference on Multimedia Technology

作者： Travis R. Gault Eslam Mostafa Ahmed A. Farag Aly A. Farag Computer Vision and Image Processing Laboratory University of Louisville Louisville KY USA

ISBN: (纸本)9781612847719

Numerous studies have investigated the functionality of the human facial recognition mechanisms with aims to improve computer vision methodologies. Understanding these mechanisms is important for applications in computer vision, as one goal of researchers is to create automatic facial recognition systems to equal the robustness of the human facial recognition system. Work in the field of human facial recognition shows that humans are able to recognize faces under very challenging scenarios, including partial occlusion. However, most human facial recognition experiments are conducted in the visible and near-infrared spectrums. During human facial recognition experiments with thermal images conducted in the lab, we found that removing the lower face yields better recognition results than when using the entire face. These tests included standard facial recognition algorithms and the results showed Rank 1 Recognition improvements of 10-25%, depending on the amount of occlusion derived from certain anatomical landmarks.

关键词： Face recognition Humans Skin Imaging Nose Probes Shape

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Non-metric calibration of camera lens distortion

Non-metric calibration of camera lens distortion

引用

IEEE International Conference on image processing

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

This paper addresses the problem of calibrating camera lens distortion, which can be significant in medium to wide angle lenses. Our approach is based on the analysis of distorted images of straight lines. We derive a new distortion measure that can be optimized using nonlinear search techniques to find the best distortion parameters that straighten these lines. Unlike the other existing approaches, we also show how to use this measure to find fast, closed-form solutions to the distortion coefficients. Some experiments to evaluate the performance of this approach on synthetic and real data are reported.

关键词： Calibration Cameras Lenses Nonlinear distortion Layout Distortion measurement Closed-form solution Geometry computer vision image processing

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