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Conference on computer vision and Pattern Recognition (CVPR)

作者： M.S. Hassouna A.A. Farag Computer Vision and Image Processing Laboratory (CVIP) University of Louisville Louisville KY USA

In this paper, we present a novel framework for computing centerlines for both 2D and 3D shape analysis. The framework works as follows: an object centerline point is selected automatically as the point of global maximum Euclidean distance from the boundary, and is considered a point source (Ps) that transmits a wave front that evolves over time and traverses the object domain. The front propagates at each object point with a speed that is proportional to its Euclidean distance from the boundary. The motion of the front is governed by a nonlinear partial differential equation whose solution is computed efficiently using level set methods. Initially, the P/sub S/ transmits a moderate speed wave to explore the object domain and extract its topological information such as merging and extreme points. Then, it transmits a new front that is much faster at centerline points than non central ones. As a consequence, centerlines intersect the propagating fronts at those points of maximum positive curvature. Centerlines are computed by tracking them, starting from each topological point until the Ps is reached, by solving an ordinary differential equation using an efficient numerical scheme. The proposed method is computationally inexpensive, handles efficiently objects with complex topology, and computes centerlines that are centered, connected, one point thick, and less sensitive to boundary noise. In addition, the extracted paths form a tree graph without additional cost. We have extensively validated the robustness of the proposed method both quantitatively and qualitatively against several 2D and 3D shapes.

关键词： Robustness Level set Shape Euclidean distance Partial differential equations Data mining Merging Differential equations Topology Noise shaping

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Robust skeletonization using the fast marching method

Robust skeletonization using the fast marching method

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IEEE International Conference on image processing

作者： M.S. Hassouna A.A. Farag Computer Vision and Image Processing Laboratory (CVIP) University of Louisville Louisville KY USA

We have recently developed a level set based-framework for computing medial curves or curve skeletons CS for arbitrary 2D shapes as well as tubular and articulated 3D objects. The proposed framework is robust, fully automatic, computationally efficient, and produces curve skeletons that are connected, centered, thin, and less sensitive to boundary noise. In this paper, we introduce two improvements to the framework in order to enhance its performance in terms of stability and topology preserving.

关键词： Robustness Skeleton Shape Topology Cost function Level set Stability Partial differential equations computer vision image processing

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Stochastic segmentation of blood vessels from time-of-flight

Stochastic segmentation of blood vessels from time-of-flight

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IEEE International Conference on image processing

作者： M.S. Hassouna A.A. Farag Computer Vision and Image Processing Laboratory (CVIP) University of Louisville Louisville KY USA

In this paper, we present an automatic statistical approach for extracting 3D blood vessels from time-of-flight (TOF) magnetic resonance angiography (MRA) data. The voxels of the dataset are classified as either blood vessels or background noise. The observed volume data is modeled by two stochastic processes. The low level process characterizes the intensity distribution of the data across the volume, while the high level process characterizes the statistical dependence among neighboring voxels. 3D Markov random field (MRF) has been employed to model the high level process, whose parameters are estimated using the maximum pseudo likelihood estimator (MPLE). Our proposed model exhibits a good fit to the clinical data and is extensively tested on different synthetic vessel phantoms and several TOF datasets. Experimental results showed that the proposed model is capable of delineating vessels down to 3 voxel diameters.

关键词： Stochastic processes Blood vessels Stochastic resonance Data mining Magnetic resonance Angiography Background noise Markov random fields Parameter estimation Testing

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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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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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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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Intensity and region-of-interest-based finite-element modeling of brain deformation

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International Congress Series 2004年第C期1268卷 373-377页

作者： Shi, Hongjian Farag, Aly Computer Vision Image Processing Laboratory Electrical Computer Engineering University of Louisville Louisville KY 40292 403 Lutz Hall Building United States

This paper proposes intensity and region-of-interest-based finite-element modeling of brain deformation. The modeling is based on the intensities of voxels within each element and motion analysis of region of interest (ROI) for image-guided minimally invasive surgery (MIS) applications. It is proposed to configure an ROI at the tip of surgical tools instead of the whole brain, so that the FEM is applied only on the ROI, and the maximum nodal displacement outside the ROI is small and in control. The deformed MR image using the proposed finite-element modeling shows better proportion to its original structures. © 2004, Elsevier Science B.V. All rights reserved.

关键词： Brain deformation Finite-element modeling Minimal invasive surgery

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