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检索条件"机构=Computer Vision Image Processing Laboratory"

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A fast level set algorithm for shape-based segmentation with multiple selective priors

A fast level set algorithm for shape-based segmentation with...

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 paper addresses the shape-based segmentation problem using level sets. In particular, we propose a fast algorithm to solve the piece-wise constant Chan-Vese segmentation model with shape priors and labeling functions. Instead of directly solving the underlying PDE, we calculate the energy and check how it changes when we move image points from inside the region enclosed by the evolving interface to the outside region and vice-vera. This algorithm is then extended to the case of multi-phase Chan-Vese model, with multiple selective shape priors and a corresponding labeling function for each prior. This makes our algorithm different from that in [1] and other similar works in different aspects. On one hand, our algorithm is not restricted to two regions, but allows segmentation into several regions. On the other hand, more than one shape prior can be taken into account in our implementation. In addition, the proposed algorithm improves dramatically the computational speed. Experimental results, on both synthetic and real images, demonstrate the performance of our algorithm and the computational improvements it offers.

关键词： Level set image segmentation Shape Labeling computer vision image processing Laboratories Biomedical imaging Application software Noise shaping

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A new symmetric shape from shading algorithm with an application to 3-D face reconstruction

A new symmetric shape from shading algorithm with an applica...

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IEEE International Conference on image processing

作者： Abdelrehim Ahmed Aly Farag Thomas Starr Computer Vision and Image Processing Laboratory (CVIP) University of Louisville Louisville KY USA

This paper presents a new symmetric shape from shading (SFS) algorithm where the self-ratio image irradiance equation proposed by Zhao and Chellappa is formulated as a partial differential equation (PDE) with a Dirichlet boundary condition. This PDE is solved using the Lax-Friedrichs sweeping method. The potential of the proposed symmetric SFS algorithm is demonstrated by several experiments on synthetic and real data. As an application, the proposed algorithm is used for 3-D face reconstruction.

关键词： Shape Partial differential equations Iterative algorithms Rough surfaces Surface roughness Face image reconstruction Differential equations Brightness Nonlinear equations

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Density estimation using a new AIC-type criterion and the EM algorithm for a linear combination of Gaussians

Density estimation using a new AIC-type criterion and the EM...

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IEEE International Conference on image processing

作者： Asem M. Ali Aly A. Farag Computer Vision and Image Processing Laboratory (CVIP Lab) University of Louisville Louisville KY USA

We propose a new approach that approximates an empirical probability density function of scalar data with a linear combination of Gaussians (LCG). The proposed algorithm approximates the marginal density of each class using a Gaussian distribution. Number of the classes and their distributions parameters are estimated using a new Akaike Information Criterion (AlC)-type criterion and the Expectation- Maximization (EM) approach. Each class does not follow perfect Gaussian distribution so we refine the initial LCG model using a modified EM algorithm. The modified EM algorithm approximates the marginal density of each class using a LCG with positive and negative components. Experiments in segmenting multimodal medical images show that the developed technique gives promising accurate results.

关键词： Gaussian processes Function approximation Probability density function Gaussian approximation Gaussian distribution Parameter estimation image processing Clustering algorithms image segmentation Probability distribution

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A novel framework for N-D multimodal image segmentation using graph cuts

A novel framework for N-D multimodal image segmentation usin...

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IEEE International Conference on image processing

作者： Asem M. Ali Aly A. Farag Computer Vision and Image Processing Laboratory (CVIP Lab) University of Louisville Louisville KY USA

This work proposes a new MAP-based segmentation framework of multimodal images. In this work a joint MGRF model is used to describe the image. The main focus here is a more accurate model identification. For a known number of classes in the given image, the empirical distributions of this image signals are precisely approximated by a LCG distributions with positive and negative components. Gibbs potential, which is used to identify the spatial interaction between the neighboring pixels, is analytically estimated. Finally, an energy function using the previous models is formulated and is globally minimized using graph cuts. Experiments show that the developed technique gives promising accurate results compared to other known algorithms.

关键词： image segmentation Pixel Joining processes Labeling image processing Gray-scale Robustness Object segmentation computer vision Laboratories

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Analytical method for MGRF Potts model parameter estimation

Analytical method for MGRF Potts model parameter estimation

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International Conference on Pattern Recognition

作者： Asem M. Ali Aly A. Farag Georgy Gimel'farb Computer Vision and Image Processing Laboratory University of Louisville USA Department of Computer Science University of Auckland New Zealand

ISBN: (纸本)9781424421749

This paper proposes a new analytical method for estimating parameters of a homogeneous isotropic Potts model with an asymmetric Gibbs potential function. The model is generalized by including both pairwise and triple cliques. The maximum likelihood estimates of the cliques potentials are obtained by a further elaboration of the approximate analytical estimator proposed in. Experiments with synthetic textures have shown that our potential estimates are more accurate and practicable than their counterparts obtained with classical methods.

关键词： Parameter estimation Maximum likelihood estimation Equations image analysis computer vision Probability distribution Pixel Least squares methods image processing Laboratories

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A kidney segmentation approach from DCE-MRI using level sets

A kidney segmentation approach from DCE-MRI using level sets

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

作者： H. Abdelmunim Aly A. Farag W. Miller Mohamed AboelGhar Department of Computer Science Computational Biomedicine Laboratory University of Houston Houston TX USA Computer Vision and Image Processing LabDepartment of Electrical and Computer Engineering University of Louisville Louisville KY USA Medical School Mansoura University Mansoura Egypt

Acute rejection is the most common reason of graft failure after kidney transplantation, and early detection is crucial to survive the transplanted kidney function. Automatic classification of normal and acute rejection transplants from dynamic contrast enhanced magnetic resonance imaging (DCEMRI), is of great importance. Kidney segmentation is the first step for such classification. The image intensity inside the kidney is used as an indication of failure/success. Differentiating between different cases cases is implemented by comparing subsequential kidney scans signals. So, this process is mainly dependent on segmentation. This paper introduces a new shape-based segmentation approach based on level sets. Training shapes are collected from different real data sets to represent the shape variations. Signed distance functions are used to represent these shapes. The methodology incorporates image and shape prior information in a variational framework. The shape registration is considered the backbone of the approach where more general transformations can be used to handle the process. We introduce a novel shape dissimilarity measure that enables the use of different (inhomogeneous) scales. The approach gives successful results compared with other techniques restricted to transformations with homogeneous scales. Results for segmenting kidney images will be illustrated and compared with other approaches to show the efficiency of the proposed technique.

关键词： Level set image segmentation computer vision Magnetic resonance imaging Shape measurement Biopsy Biomedical computing computer science image processing Biomedical imaging

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Lighting analysis and texture modification of 3D human face scans

Lighting analysis and texture modification of 3D human face ...

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Australian Pattern Recognition Society (APRS)

作者： Xiaozheng, Zhang Sanqiang, Zhao Yongsheng, Gao Computer Vision and Image Processing Laboratory Griffith School of Engineering Griffith University 170 Kessels Road Nathan QLD 4111 Australia

ISBN: (纸本)0769530672

The 3D scanning of human faces is an important tool to acquire accurate shape and texture information of heads and faces for both animation and recognition purposes. One of the most popular scanning tools is Cyberware head and face 3D colour scanner. Currently, 3D face databases scanned using the Cyberware head and face 3D colour scanner databases are publicly available and have been used in many face recognition system as the training data, one of which is USF human ID 3-D database. The mechanism of facial texture capturing, however, remains unclear due to the special settings of the light sources during scanning. The direct application of the acquired texture intensity as the albedo of the face surfaces leads to suboptimal modelling results. This paper analyses the lighting configuration of the scanner and proposes an appropriate reflectance model to describe the reflecting mechanism when scanning. The texture modifications can be carried on in the existing scanning results without any further scans. Experiment results show that the proposed reflectance model can better approximate the reflecting situation of scanning so that the acquired textures can be directly used as diffuse reflectance coefficients and achieve better rendering results. © 2007 IEEE.

关键词： Scanning

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Experiments on sensitivity of template matching for lung nodule detection in low dose CT scans

Experiments on sensitivity of template matching for lung nod...

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ISSPIT 2007 - 2007 IEEE International Symposium on Signal processing and Information Technology

作者： Elhabian, Shireen Y. El Munim, Hossam Abd Elshazly, Salwa Farag, Aly A. Aboelghar, Mohamed Computer Vision and Image Processing Laboratory Univ. of Louisville Louisville KY 40292 United States Urology and Nephrology Center Mansoura University Mansoura Egypt

ISBN: (纸本)9781424418350

Template matching is a common approach for detection lung nodules from CT scans. Templates may take different shapes, size and intensity distribution. The process of nodule detection is essentially two steps: isolation of candidate nodules, and elimination of false positive nodules. The processes of outlining the detected nodules and their classification (i.e., assigning pathology for each nodule) complete the CAD system for early detection of lung nodules. This paper is concerned with the template design and evaluating the effectiveness of the first step in the nodule detection process. The paper will neither address the problem of reducing false positives nor would it deal with nodule segmentation and classification. Only parametric templates are considered. Modeling the gray scale distribution for the templates based on the prior knowledge of typical nodules extracted by radiologists. The effectiveness of the template matching is investigated by cross validation with respect to the ground truth and is described by hit rate curves indicating the probability of detection as function of shape, size and orientation, if applicable, of the templates. We used synthetic and sample real CT scan images in our experiments. It is found that template matching is more sensitive to additive noise than image blurring when tests conducted on synthetic data. On the sample CT scans small size circular and hollow-circular templates provided comparable results to human experts. ©2007 IEEE.

关键词： Template matching

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Elastic Registration of the Corpus Callosum

Elastic Registration of the Corpus Callosum

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IEEE International Conference on image processing

作者： Hossam E. Abd El Munim Aly A. Farag Computer Vision and Image Processing (CVIP) Laboratory University of Louisville Louisville KY 40292. Computer Vision and Image Processing (CVIP) Laboratory University of Louisville Louisville KY USA

Geometric analysis of postmortem normal and autistic human subjects reveal distinctions in deformations in the corpus callosum (CC) that may be used for image analysis-based studies of autism. Preliminary studies showed that the CC of autistic patients is quite distinct from normal controls. We use an implicit vector representation of CC to carry out the registration process which measures the differences between different CC's. This paper introduces a new method for the 2D shape registration problem by matching vector distance functions. A variational frame work is proposed for the global and local registration of CC's. A gradient descent optimization is used which can efficiently handle both the rigid and the non-rigid operations together. The registration of real CC extracted from postmortem data sets demonstrates the potential of the proposed approach.

关键词： Shape measurement Autism computer vision Magnetic resonance imaging image analysis image processing Laboratories Humans Area measurement computer graphics

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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...

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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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