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Conference on computer vision and Pattern Recognition (CVPR)

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

SIFT has been proven to be the most robust local invariant feature descriptor. SIFT is designed mainly for gray images. However, color provides valuable information in object description and matching tasks. Many objects can be misclassified if their color contents are ignored. This paper addresses this problem and proposes a novel colored local invariant feature descriptor. Instead of using the gray space to represent the input image, the proposed approach builds the SIFT descriptors in a color invariant space. The built Colored SIFT (CSIFT) is more robust than the conventional SIFT with respect to color and photometrical variations. The evaluation results support the potential of the proposed approach.

关键词： Colored noise Robustness Histograms Photometry Feature extraction Lighting computer vision image retrieval image processing laboratories

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Level Set Segmentation Using Statistical Shape Priors

Level Set Segmentation Using Statistical Shape Priors

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

作者： A.S. El-Baz A.A. Farag H.A. El Munim S.E. Yuksel Computer Vision & Image Processing Laboratory (CVIP) University of Louisville Louisville KY USA

A novel and robust 3-D segmentation approach is proposed using level sets based on shape constraints. The approach depends on both the gray level and shape information. A partial differential equation (PDE) is developed to control the evolution of the level sets. The PDE does not include weighting coefficients to be tuned, overcoming the disadvantages of other PDE approaches. The shape information is gathered from a set of the signed distance maps representing the training data as a histogram of the occurrences of the points inside and outside the object. We use a novel statistical approach to get a probability density function (pdf) for the signed distance map of the points inside and outside and also the distribution of gray level inside and outside the object. The proposed statistical approach is based on modelling the empirical density function (normalized histogram of occurrence) for either the gray level distribution or signed distance map with a linear combination of Gaussians (LCG) with positive and negative components. We modify an Expectation-Maximization (EM) algorithm to deal with the LCGs and also propose a novel EM-based sequential technique to get a close initial LCG approximation for the modified EM algorithm to start with. The pdf’s of the signed distance and intensity gray level are embedded in the speed function of the level set specifying the direction of evolution. Experimental results show how the approach is accurate in segmenting different types of 2-D and 3-D data sets including medical images.

关键词： Level set Shape Histograms Approximation algorithms Robustness Partial differential equations Training data Probability density function Density functional theory Gaussian distribution

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3D path planning for virtual endoscopy

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International Congress Series 2005年 1281卷 115-120页

作者： Hassouna, M. Sabry Farag, A.A. Hushek, Stephen G. Computer Vision and Image Processing Laboratory (CVIP) University of Louisville Louisville KY 40292 United States iMRI Department Norton Hospital Louisville KY 40202 United States

Centerline extraction of curvilinear objects is a crucial component of virtual endoscopy (VE) because it provides path planning for automatic navigation. In this paper, we present a guided voxel coding (GVC) algorithm that modifies the original method to extract reliable centerlines suitable for fly-through applications. Our proposed method extracts potential centerline voxels with maximal spheres inscribed in the object and stores them in an a priori guidance map. Centerlines are initially extracted as trajectories inside the object and then centered using a new mechanism that exploits the information contained in that map. A new level set method accurately identifies the topological branching nodes of the joint centerlines. Finally, a thinning method is proposed to guarantee single voxel width centerlines. The proposed algorithm is completely automated and generates reliable, connected, and smoothed navigation paths for complex tree structures. © 2005.

关键词： 3D path planning Flight path generation Virtual endoscopy

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Robust robotic path planning using level sets

Robust robotic path planning using level sets

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IEEE International Conference on image processing

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

In this paper, we propose a general and robust robotic path planning framework for both planar and terrain environments using 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 the entire or a portion of the configuration space. The optimum planned path can be controlled to follow the safest, shortest, or hybrid path. We have demonstrated the robustness of the proposed framework by correctly extracting all planned paths of complex maps with several obstacles.

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

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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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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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A silhouette-contour based 3-D registration methodology as a pre-evaluation step of 3-D reconstruction techniques

A silhouette-contour based 3-D registration methodology as a...

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IEEE International Conference on image processing

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

Data registration is a crucial step in performance evaluation procedures that aim at localizing errors in given measured data. Under the performance evaluation topic, the probably corrupted measured data should be accurately registered/aligned to the ground truth data such that the registration process would not affect the accuracy of the consequent evaluation steps. To cope with this problem, another registration methodology that can go beyond the conventional ones should be used. Here we propose a novel approach for 3-D data registration. The performance of the approach is totally independent of the measured data set. The key advantage of this approach is that it keeps the registration process away from being affected by the probably corrupted data sets, hence it permits confidential evaluation results.

关键词： Three dimensional displays computer vision Iterative closest point algorithm Application software Iterative algorithms Calibration image processing laboratories computer errors Object recognition

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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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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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