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

共 343 条记录，以下是121-130 订阅

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Line-Based Affine Invariant Object Location Using Transformation Space Decomposition

Line-Based Affine Invariant Object Location Using Transforma...

引用

International Conference on Pattern Recognition

作者： R. Yang Yongsheng Gao Computer Vision and Image Processing Laboratory School of Engineering Griffith University Brisbane Australia

This paper presents a novel line-based affine invariant object location methodology. Our algorithm employs a new line-based transformation space decomposition technique to exploit intrinsic structural information provided by line features. Furthermore, we propose a new line-based distance transform to integrate with our algorithm to provide efficient transformation cell evaluation and subdivision in a coarse to fine manner. The algorithm is able to rapidly accelerate the searching process while maintaining high discriminative power and minimal storage requirement. The efficiency and discriminative power of this methodology are demonstrated using real-world examples with promising results

关键词： OFDM modulation image segmentation Object detection computer vision image processing Acceleration Machine vision Detectors Pixel Pattern recognition

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A New Formulation for Shape from Shading for Non-Lambertian Surfaces

A New Formulation for Shape from Shading for Non-Lambertian ...

引用

Conference on computer vision and Pattern Recognition (CVPR)

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

Lambert’s model for diffuse reflection is a main assumption in most of shape from shading (SFS) literature. Even with this simplified model, the SFS is still a difficult problem. Nevertheless, Lambert’s model has been proven to be an inaccurate approximation of the diffuse component of the surface reflectance. In this paper, we propose a new solution of the SFS problem based on a more comprehensive diffuse reflectance model: the Oren and Nayar model. In this work, we slightly modify this more realistic model in order to take into account the attenuation of the illumination due to distance. Using the modified non-Lambertian reflectance, we design a new explicit Partial Differential Equation (PDE) and then solve it using Lax-Friedrichs Sweeping method. Our experiments on synthetic data show that the proposed modeling gives a unique solution without any information about the height at the singular points of the surface. Additional results for real data are presented to show the efficiency of the proposed method . To the best of our knowledge, this is the first non-Lambertian SFS formulation that eliminates the concave/convex ambiguity which is a well known problem in SFS.

关键词： Shape Reflectivity Cameras Lighting Brightness Optical imaging Light sources Rough surfaces Surface roughness computer vision

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Robust skeletonization using the fast marching method

Robust skeletonization using the fast marching method

引用

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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Labelling color images by modelling the colors density using a linear combination of Gaussians and EM algorithm

Labelling color images by modelling the colors density using...

引用

IEEE International Conference on image processing

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

Parametric density estimation is widely used to solve many image processing problems. We examined the parametric estimation using linear combination of 1D Gaussians in many works. In this work, we extend our model to estimate density of the colors in color images. We approximate the marginal density of each class in the empirical probability density function by a 3D Gaussian distribution. Then, the deviation between the estimated and the empirical densities is modelled using a linear combination of 3D Gaussians with positive and negative components. We estimate the parameters of this model using our modified EM algorithm. The proposed framework demonstrates very promising experimental results of color images labelling and can be integrated with many other frameworks.

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

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Morphological geodesic active contour based automatic aorta segmentation in thoracic CT images

Morphological geodesic active contour based automatic aorta ...

引用

International Conference on computer vision and image processing, CVIP 2016

作者： Dasgupta, Avijit Mukhopadhyay, Sudipta Mehre, Shrikant A. Bhattacharyya, Parthasarathi Computer Vision and Image Processing Laboratory Department of Electronics and Electrical Communication Engineering Indian Institute of Technology Kharagpur West Bengal721302 India Institute of Pulmocare & Research KolkataWest Bengal700156 India

ISBN: (纸本)9789811021060

Automatic aorta segmentation and quantification in thoracic computed tomography (CT) images is important for detection and prevention of aortic diseases. This paper proposes an automatic aorta segmentation algorithm in both contrast and non-contrast CT images of thorax. The proposed algorithm first detects the slice containing the carina region. Circular Hough Transform (CHT) is applied on the detected slice to localize ascending and descending aorta (circles with lowest variances) followed by a morphological geodesic active contour to segment the aorta from CT stack. The dice similarity coefficients (DSC) between the ground truth and the segmented output were found to be 0.8845 ± 0.0584 on LIDC-IDRI dataset. © Springer Science+Business Media Singapore 2017.

关键词： image segmentation

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Precise change detection in multi-spectral remote sensing imagery using SIFT-based registration

Precise change detection in multi-spectral remote sensing im...

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International Conference on Multimedia Technology

作者： Mostafa Abdelrahman Asem Ali Aly A. Farag Computer Vision & Image Processing Laboratory (CVIP) University of Louisville Louisville KY USA

ISBN: (纸本)9781612847719

In this paper we propose a robust method for geometric co registration, and an accurate change detection technique based on statistical method for multi-temporal high-resolution satellite imagery. Lhe proposed algorithm is as the following: scale-invariant feature transform (SIFT) is used to extract a set of correspondence points in a pair, or multiple pairs, of images that are taken at different times and under different circumstances, then Random Sample Consensus (RANSAC) is used to remove the outlier set. the resulting inliers matched points is an accurate correspondences which used to register the given images, changes in registered images are identified using statistical analysis of image differences. Finally, Markov-Gibbs Random Field (MGRF) is used to model the spatial-contextual information contained in the resulting change mask. Experiments with generated synthetic multiband images, and LANDSAT5 images, approved the accuracy the proposed algorithm.

关键词： Remote sensing Satellites Earth Detection algorithms Feature extraction Statistical analysis Accuracy

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Stochastic segmentation of blood vessels from time-of-flight

Stochastic segmentation of blood vessels from time-of-flight

引用

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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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...

引用

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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Super-resolution in the presence of space-variant blur

Super-resolution in the presence of space-variant blur

引用

International Conference on Pattern Recognition

作者： K.V. Suresh A.N. Rajagopalan Image Processing and Computer Vision Laboratory Department of Electrical Engineering IIT Madras Chennai India

An efficient algorithm using maximum a posteriori-Markov random field (MAP-MRF) based approach for recovering a high-resolution image from multiple sub-pixel shifted low-resolution images is proposed. The algorithm can be used for super-resolution of both space-invariant and space-variant blurred images. We prove an important theorem that the posterior is also Markov and derive the exact posterior neighborhood structure in the presence of warping, blurring and down-sampling operations. The posterior being Markov enables us to perform all matrix operations as local image domain operations thereby resulting in a considerable speedup. Experimental results are given to demonstrate the effectiveness of our method

关键词： Spatial resolution image resolution Cameras image processing computer vision Laboratories Layout Frequency domain analysis Markov random fields Reconstruction algorithms

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