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International Conference on Signal processing, Communication and Networking (ICSCN)

作者： Rahul Dey P. K. Nanda Sucheta Panda Image Processing and Computer Vision Lab Department of Electrical Engineering National Institute of Technology Rourkela Orissa India Department of Electronics & Telecommunication Engineering C.V. Raman College of Engineering Bhubaneswar Orissa India

In this paper, the problem of color image segmentation is addressed as a pixel labeling problem. The observed color image is assumed to be the degraded version of the image labels. We have proposed a new Markov random field (MRF) model known as constrained MRF (CMRF) model to model the unknown image labels and Ohta (I 1 I 2 I 3 ) model is used as the color model. The unique feature of the proposed CMRF model is found to posses a unifying feature of modeling scene and texture images as well. The labels are estimated using maximum a posteriori (MAP) estimation criterion. A hybrid algorithm is proposed to obtain the MAP estimate and the performance of the algorithm is found to be better than that of using simulated annealing (SA) algorithm. The performance of the proposed model is compared with JSEG method and the proposed model is found to be better than JSEG method.

关键词： Markov random fields image segmentation Color Hidden Markov models Pixel Degradation Layout Signal processing algorithms image converters labeling

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High Resolution image Reconstruction in Shape from Focus

High Resolution Image Reconstruction in Shape from Focus

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IEEE International Conference on image processing

作者： R. R. Sahay A. N. Rajagopalan Image Processing and Computer Vision Lab Department of Electrical Engineering Indian Institute of Technology Madras Chennai India

In the Shape from Focus (SFF) method, a sequence of images of a 3D object is captured for computing its depth profile. However, it is useful in several applications to also derive a high resolution focused image of the 3D object. Given the space-variantly blurred frames and the depth map, we propose a method to optimally estimate a high resolution image of the object within the SFF framework.

关键词： image resolution image reconstruction Shape Focusing Cameras Apertures Pixel computer vision image processing Application software

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Automatic detection of renal rejection after kidney transplantation

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International Congress Series 2005年 1281卷 773-778页

作者： Yuksel, S.E. El-Baz, A. Farag, A.A. Abo El-Ghar, M.E. Eldiasty, T.A. Ghoneim, M.A. Computer Vision and Image Processing Lab University of Louisville Louisville KY 40292 United States Mansoura University Urology and Nephrology Center Mansoura Egypt

Acute rejection is the most important reason of graft failure after kidney transplantation, and early detection is crucial to survive the kidney function. Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) is an imaging modality where a contrast agent (such as Gd-DTPA) is introduced into the kidney and rapid and repeated images are taken, which allows both the anatomic and functional information of the kidney to be monitored. Using these images, this paper introduces an accurate method for the segmentation of the kidney from the surrounding structures by introducing a shape model of the kidney into the external energy component of the deformable models. Secondly, registration algorithms are employed to account for the motion of the kidney due to patient breathing and finally, the perfusion curves obtained from the cortex that show the transportation of the contrast agent into the tissue are given for future classification of normal and acute rejection transplants. Applications of the proposed algorithms yield promising results. © 2005.

关键词： Contrast agents Deformable models Dynamic MRI Gd-DTPA Kidney transplantation Mutual information Renal rejection

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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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Proceedings - 10th IEEE International Conference on computer vision, ICCV 2005

作者： El-Melegy, Moumen T. Al-Ashwal, Nagi H. Farag, Aly A. Electrical Engineering Department Assiut University Assiut 71516 Egypt Computer Vision and Image Processing Lab. University of Louisville KY 40292

ISBN: (纸本)076952334X

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. © 2005 IEEE.

关键词： image segmentation

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Robust virtual forces-based camera positioning using a fusion of image content and intrinsic parameters

Robust virtual forces-based camera positioning using a fusio...

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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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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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Statistically Robust Approach to Lens Distortion Calibration with Model Selection

Statistically Robust Approach to Lens Distortion Calibration...

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Conference on computer vision and Pattern Recognition Workshop, CVPRW 2003

作者： Taha El-Melegy, Moumen Farag, Aly A. Electrical Engineering Dept. Assiut University Assiut Egypt Computer Vision and Image Processing Lab. University of Louisville LouisvilleKY40292 United States

ISBN: (纸本)0769519008

This paper addresses the problem of calibrating camera lens distortion, which can be significant in medium to wide angle lenses. While almost all existing nonmetric distortion calibration methods need user involvement in one form or another,we present an approach to distortion calibration based on the robust the-least-median-of-squares (LMedS) estimator. Our approach is thus able to proceed in a ful ly-automatic manner while being less sensitive to erroneous input data such as image curves that are mistakenly considered as projections of 3D linear segments. Our approach uniquely uses fast, closed-form solutions to the distortion coefficients, which serve as an initial point for a non-linear optimization algorithm to straighten imaged lines. Moreover we propose a method for distortion model selection based on geometrical *** experiments to evaluate the performance of this approach on synthetic and real data are reported. © 2003 IEEE.

关键词： Least squares approximations

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Statistically Robust Approach to Lens Distortion Calibration with Model Selection

Statistically Robust Approach to Lens Distortion Calibration...

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

作者： Moumen Taha El-Melegy Aly A. Farag Computer Vision and Image Processing Laboratory University of Louisville Louisville KY USA Electrical Engineering Dept. Assiut University Assiut Egypt Computer Vision and Image Processing Lab. University of Louisville Louisville KY

关键词： Nonlinear distortion Calibration Robustness Lenses Cameras image edge detection Distortion measurement

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Multiple objects segmentation based on maximum-likelihood estimation and optimum entropy-distribution(MLE-OED)

Multiple objects segmentation based on maximum-likelihood es...

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16th International Conference on Pattern Recognition, ICPR 2002

作者： Jun, Xie Tsui, H.T. Deshen, Xia Image Processing and Computer Vision Lab Department of Electronic Engineering Chinese University of Hong Kong Hong Kong Pattern Recognition Lab Department of Computer Science Nanjing University of Sci.andTech. China

ISBN: (纸本)0769516963

A new method based on MLE-OED is proposed for unsupervised image segmentation of multiple objects which have fuzzy edges. It adjusts the parameters of a mixture of Gaussian distributions via minimizing a new loss function proposed to implement image segmentation based on the image's local spatial information and global intensity distribution properties . The loss function consists of two terms: a local content fitting term, which optimizes the entropy distribution, and a global statistical fitting term, which maximizes the likelihood of the parameters for the given data. The proposed segmentation method was validated by simulated and real examples Its performance in the experiments is better than those of two popular methods. © 2002 IEEE.

关键词： Maximum likelihood estimation

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