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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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Active contours: an overview with applications to motion artifact cancellation in MRI

Active contours: an overview with applications to motion art...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

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

Motion can be estimated by detecting the edges of a moving object using active contours, and registering them together to obtain the motion model parameters. This idea can be applied to patient motion during the acquisition of an MRI to eliminate motion artifacts in the image. The data obtained during the MRI acquisition, the k-space, can be divided into several subbands such that each subband is acquired in a small fraction of the full imaging time. These subbands create invariant tissue feature maps called subband images. Using active contours, the relative motion is analyzed across the different subband images to determine the motion parameters. Using these motion parameters it is possible to correct the subbands, thus correcting the k-space. This has the potential to yield clear, noise-free MR images.

关键词： Active contours Magnetic resonance imaging image edge detection computer vision Motion estimation Object detection Motion detection Motion analysis Gray-scale Application software

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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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Integrating shape from shading and range data using neural networks

Integrating shape from shading and range data using neural n...

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Conference on computer vision and Pattern Recognition (CVPR)

作者： M.G.-H. Mostafa S.M. Yamany A.A. Farag Computer Vision and Image Processing Lab EE Department University of Louisville Louisville KY USA Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt

This paper presents a framework for integrating multiple sensory data, sparse range data and dense depth maps from shape from shading in order to improve the 3D reconstruction of visible surfaces of 3D objects. The integration process is based on propagating the error difference between the two data sets by fitting a surface to that difference and using it to correct the visible surface obtained from shape from shading. A feedforward neural network is used to fit a surface to the sparse data. We also study the use of the extended Kalman filter for supervised learning and compare it with the backpropagation algorithm. A performance analysis is done to obtain the best neural network architecture and learning algorithm. It is found that the integration of sparse depth measurements has greatly enhanced the 3D visible surface obtained from shape from shading in terms of metric measurements.

关键词： Neural networks Surface fitting Layout Surface reconstruction Sensor phenomena and characterization computer vision image reconstruction Shape measurement Machine vision Supervised learning

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Active contours: an overview with applications to motion artifact cancellation in MRI

Active contours: an overview with applications to motion art...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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

Motion can be estimated by detecting the edges of a moving object using Active Contours, and registering them together to obtain the motion model parameters. This idea can be applied to patient motion during the acquisition of an MRI to eliminate motion artifacts in the image. The data obtained during the MRI acquistion, the k-space, can be divided into several subbands such that each subband is acquired in a small fraction of the full imaging time. These subbands create invariant tissue feature maps called subband images. Using Active Contours, the relative motion is analyzed across the different subband images to determine the motion parameters. Using these motion parameters it is possible to correct the subbands, thus correcting the k-space. This has the potential to yield clear, noise-free MR images.

关键词： Active contours Magnetic resonance imaging image edge detection computer vision Motion estimation Object detection Motion detection Motion analysis Gray-scale Application software

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Analysis of texture images using robust fractal description

Analysis of texture images using robust fractal description

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1995 IEEE Southwest Symposium on image Analysis and Interpretation, IAI 1994

作者： Avadhanam, N. Mitra, S. Center for Image Processing and Integrated Computing Dept. of Electr. and Comput. Eng. California Univ. DavisCA95616 United States Computer Vision and Image Analysis Lab Dept. of Electrical Engineering Texas Tech University LubbockTX79401 United States

ISBN: (纸本)0818662506

A maximum likelihood estimation (MLE) method is used to estimate the fractal dimension of a number of natural texture images with and without the presence of noise. An additional texture measure which can be linked to the lacunarity measure is used to characterize natural textures since fractal dimension alone cannot totally characterize texture images. Segmentation of natural textures is successfully achieved by a k-means clustering algorithm using fractal dimension and the additional measure as representative features. © 1994 IEEE.

关键词： image texture

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Analysis of texture images using robust fractal description

Analysis of texture images using robust fractal description

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IEEE Southwest Symposium on image Analysis and Interpretation

作者： N. Avadhanam S. Mitra Center for Image Processing & Integrated Computing Department of Electrical & Computer Engineering University of California Davis CA USA Computer Vision and Image Analysis Lab Department of Electrical Engineering Texas Tech University Lubbock TX USA

关键词： image analysis image texture analysis Robustness Fractals Maximum likelihood estimation image segmentation Polymers image motion analysis computer vision Clustering algorithms

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On empirical estimation of the parameters of edge enhancement filters

On empirical estimation of the parameters of edge enhancemen...

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IEEE International Conference on Systems, Man, and Cybernetics, SMC 1992

作者： Farag, Aly A. Cao, Yu Rose, D.M. Delp, Edward J. Engineering Mathematics and Computer Science Dept. Speed Scientific School University of Louisville LouisvilleKY40292 United States Computer Vision and Image Processing Lab. School of Electrical Engineering Purdue University West LafayetteIN47907 United States

ISBN: (纸本)0780307208

Edge-based image segmentation is a two-stage process;edge enhancement followed by edge linking. Modern approaches for edge enhancement use either the gradient of the Gaussian operator (VG) or the Laplacian of the Gaussian operator (∇2G). Both operators maximize the energy of the output and both can be performed as the cascade of two operations: a filtering operation with a Gaussian filter followed by a differentiation operation (∇ or ∇2). The filtering operation provides the smoothing or the regularization needed to transform numerical differentiation from an ill-posed problem to a well-posed one. The Gaussian filter is specified by one parameter only, the standard deviation σ. The filter's spatial support is a function of σ. In this paper, an empirical measure is described for the selection of the Gaussian filter's spatial support using the power spectrum density of the input image. © 1992 IEEE.

关键词： image segmentation

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Medial axis representation and encoding of scanned documents

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Journal of Visual Communication and image Representation 1991年第2期2卷 151-165页

作者： Brandt, Jonathan W. Jain, Anil K. Ralph Algazi, V. Computer Vision Research Lab CIPIC: The Center for Image Processing and Integrated Computing United States Department of Electrical Engineering and Computer Science University of California Davis CA 95616 United States

The medial axis transform (MAT) is a sparse representation of shape, which, being reversible, has potential for binary image compression. The MAT also provides structural information not accessible with alternative binary image codes. This structural information is often used for line extraction and pattern recognition. Therefore, the MAT occupies an interesting middle ground between lower-level, compressible image codes and higher-level, meaningful image representations. In an attempt to realize this middle ground, a MAT-based coding scheme which respects its structural properties is developed. The MAT-based coding method is applied to the CCITT facsimile test images. The resulting compression is comparable to that of known coding schemes for textual and somewhat better for graphical data. Some simple modifications to improve skeleton coding performance are suggested. To substantiate the claim that the MAT is effective for higher-level processing, the representation is extended to perform line segment and curve extraction. The extraction takes place under the control of a global error tolerance parameter and thus provides a new method to trade off data compression with distortion for binary images. © 1991.

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