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Proceedings - International Conference on Pattern Recognition 2002年第1期16卷 707-710页

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. and Tech. China

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： Object recognition

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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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International Conference on Pattern Recognition

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

ISBN: (纸本)076951695X

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. The performance in the experiments is better than those of two popular methods.

关键词： Object segmentation Maximum likelihood estimation image segmentation Layout Uncertainty image edge detection Shape measurement image processing Pattern recognition Gaussian distribution

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image approximation and smoothing by support vector regression

Image approximation and smoothing by support vector regressi...

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International Joint Conference on Neural Networks (IJCNN)

作者： D. Kai Tik Chow Tong Lee Computer Vision and Image Processing Laboratory Department of Electronic Engineering Chinese University of Hong Kong Hong Kong China

ISBN: (纸本)0780370449

A new image representation by support vector regression (SVR) is introduced. After a grey level image is approximated as a continuous function using SVR, which maps a 2D pixel coordinate input into a 1D pixel grey level output, the image can then be expressed in terms of the extracted support vectors and their corresponding Lagrange multipliers. The image is reconstructed by a linear combination of kernels with weights equal to the values of Lagrange multipliers. With support vector representation, we can observed that: 1) it is able to remove noise from image, the denoising effect of SVR representation is implicit during image encoding, and it can be controlled by the SVR training parameters; 2) if a Gaussian RBF kernel is used in SVR representation, Gaussian smoothing can be easily implemented by increasing the variance of kernel during image reconstruction and sharpening can be done by reducing the variance.

关键词： Smoothing methods Kernel Pixel Vectors Lagrangian functions image reconstruction Noise reduction image representation Gaussian noise image coding

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Optimization on unbounded solution space using dynamic genetic algorithms

Optimization on unbounded solution space using dynamic genet...

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International Joint Conference on Neural Networks (IJCNN)

作者： Chi Kin Chow Hung Tat Tsui Tong Lee Computer Vision and Image Processing Laboratory Department of Electronic Engineering Chinese University of Hong Kong Sha Tin Hong Kong China

ISBN: (纸本)0780370449

The problem of applying genetic algorithm (GA) to solve an optimization problem over an unbounded solution space is addressed. We propose to first transform the possible range of each parameter in the chromosome to a finite range with a nonlinear mapping such that the search on unbounded solution space becomes a search for high precision solution in a finite range. Modifications on the GA have been found necessary after such nonlinear mapping. As a result, a new GA with dynamic mutation range that facilitates coarse-refine search has been developed.

关键词： Genetic algorithms Biological cells Optimization methods Genetic mutations computer vision image processing Laboratories Genetic engineering Chromosome mapping Dynamic range

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Multimodality image registration and fusion using neural network

Multimodality image registration and fusion using neural net...

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3rd International Conference on Information Fusion, FUSION 2000

作者： Mostafa, Mostafa G. Farag, Aly A. Essock, Edward Computer Vision and Image Processing Laboratory Department of Electrical and Computer Engineering University of Louisville Louisville KY 40292 United States Department of Psychology University of Louisville Louisville KY 40292 United States

ISBN: (纸本)2725700000

Multi-modality image registration and fusion are essential steps in building 3D models from remote sensing data. In this paper, we present a neural network technique for the registration and fusion of multi-modality remote sensing data for the reconstruction of 3D models of terrain regions. A feedforward neural network is used to fuse the intensity data sets with the spatial data set after learning its geometry. Results on real data are presented. Human performance evaluation is assessed on several perceptual tests in order to evaluate the fusion results. © 2000 Int. Soc. Inf. Fusion.

关键词： image fusion

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Multimodality image registration and fusion using neural network

Multimodality image registration and fusion using neural net...

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International Conference on Information Fusion

作者： M.G. Mostafa A.A. Farag E. Essock Department of Electrical and Computer Engineering Computer Vision and Image Processing Laboratory University of Louisville Louisville KY USA Department of Psychology University of Louisville Louisville KY USA

关键词： image registration Neural networks Hyperspectral sensors Remote sensing image reconstruction Geometry Remote monitoring Intelligent sensors Satellites Laser radar

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Multi-modal medical volumes fusion by surface matching

Multi-modal medical volumes fusion by surface matching

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5th International Symposium on Signal processing and Its Applications, ISSPA 1999

作者： Eldeib, Ayman M. Yamany, Sameh M. Farag, Aly Computer Vision and Image Processing Laboratory University of Louisville Department of Electrical Engineering Louisville KY 40292 United States

ISBN: (纸本)1864354518

Presents a fast, six degrees of freedom, registration technique to accurately locate the position and orientation of medical volumes (e.g. CT, MRI) with respect to each other for the same patient. The technique uses surface registration and maximization of mutual information. We have developed a novel technique for surface registration which produces highly accurate results when registering two different volumes of the same individual generated from the same modality, such as preoperative MR and intraoperative MR volumes. In case surface registration is not able to accurately register the different volumes, the result is enhanced by multi-modal volume registration. The gain of this combination is to have an accurate alignment and to reduce time needed for registration. For the multi-modal volume registration, the maximization of mutual information (MI) as a matching criterion is used based on genetic algorithms (GA) as a search engine. Our results demonstrate that our registration technique allows for fast, accurate, robust and completely automatic registration of multimodality medical volumes. © 1999 IEEE.

关键词： Genetic algorithms

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Free-form surface registration using surface signatures

Free-form surface registration using surface signatures

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International Conference on computer vision (ICCV)

作者： S.M. Yamany A.A. Farag Computer Vision and Image Processing Laboratory Electrical Engineering Department University of Louisville Louisville KY USA

The paper introduces a new free-form surface representation scheme for the purpose of fast and accurate registration and matching. Accurate registration of surfaces is a common task in computer vision. The proposed representation scheme captures the surface curvature information seen from certain points and produces images called surface signatures at these points. Matching signatures of different surfaces enables the recovery of the transformation parameters between these surfaces. We propose to use template matching to compare the signature images. To enable partial matching, another criterion, the overlap ratio, is used. This representation scheme can be used as a global representation of the surface as well as a local one and performs near real time registration. We show that the signature representation can be used to match objects in 3D scenes in the presence of clutter and occlusion. Applications presented include free-form object matching, multimodal medical volume registration and dental teeth reconstruction from intra-oral images.

关键词： Shape computer vision Surface reconstruction Application software Biomedical imaging image processing Layout Dentistry Teeth image reconstruction

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Multi-modal medical volumes fusion by surface matching

Multi-modal medical volumes fusion by surface matching

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International Symposium on Signal processing and Its Applications (ISSPA)

作者： A.M. Eldeib S.M. Yamany A. Farag Computer Vision and Image Processing Laboratory Department of Electrical Engineering University of Louisville Louisville KY USA

关键词： Biomedical imaging Medical diagnostic imaging Computed tomography Mutual information Magnetic resonance imaging Data mining Magnetic resonance Genetic algorithms Shape computer vision

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Using active contours and Fourier descriptors for motion tracking with applications in MRI

Using active contours and Fourier descriptors for motion tra...

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IEEE International Conference on image processing

作者： D.L. Guidry A.A. Farag Computer Vision and Image Processing Lab Electrical Engineering Department University of Louisville Louisville KY USA

To estimate motion, the edges of a moving object can be detected using active contours. After detecting the edges throughout the duration of the motion, the contours are registered together in order to obtain the motion model parameters. This idea can be applied to motion artifacts in Magnetic Resonance images (MRI) which are caused by patient motion during the acquisition of an MRI. Thus, if the patient motion can be determined, it will be possible to correct for the motion in the images. The proposed method eliminates motion artifacts by dividing the acquired MRI data into several subbands. These subbands create invariant tissue feature maps called subband images. Using active contours, the edges of each subband image are detected and the relative motion is analyzed across the different subband images, thus determining the motion parameters. From these motion parameters it is possible to correct the subband images, thus correcting the k-space yielding clear, noise-free MR images.

关键词： Active contours Tracking image edge detection Motion detection Magnetic resonance imaging Motion analysis Motion estimation Object detection Magnetic resonance image analysis

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