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International Conference on Neural Networks

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

We present a system to segment and label CT/MRI brain slices using feature extraction and unsupervised clustering. In this technique, each voxel is assigned a feature pattern consisting of a scaled family of differential geometrical invariant features. The invariant feature pattern is then assigned to a specific region using a two-stage neural network system. The first stage is a self-organizing principal components analysis (SOPCA) network that is used to project the feature vector onto its leading principal axes found by using principal components analysis. This step provides an effective basis for feature extraction. The second stage consists of a self-organizing feature map (SOFM) which will automatically cluster the input vector into different regions. The optimum number of regions (clusters) is obtained by a model fitting approach. Finally, a 3D connected component labeling algorithm is applied to ensure region connectivity. Implementation and performance of this technique are presented. Compared to other approaches, the new system is more accurate in extracting 3D anatomical structures of the brain, and can be adapted to real-time imaging scenarios.

关键词： Neural networks image segmentation Biomedical imaging Feature extraction Principal component analysis Computed tomography Magnetic resonance imaging Brain Labeling Clustering algorithms

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A spatialoral parallel approach for real-time MPEG video compression 25

A spatialoral parallel approach for real-time MPEG video com...

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25th International Conference on Parallel processing, ICPP 1996

作者： Shen, Ke Delp, E.J. Purdue University School of Electrical Engineering Computer Vision and Image Processing Laboratory West LafayetteIN United States Purdue Multimedia Testbed West LafayetteIN47907-1285 United States

ISBN: (纸本)081867623X

We present a parallel implementation of an MPEG encoder on the Intel Paragon supercomputer. In our approach, both spatial and temporal parallelism have been exploited, While the Paragon has the computation capacity to encode video sequences faster than real-time, we found that real-time performance cannot be achieved if the input/output techniques are not designed properly. We present several schemes for managing the I/O and show their effect on reducing I/O contention. © 1996 IEEE.

关键词： Supercomputers

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Complexity optimization of adaptive RBF networks

Complexity optimization of adaptive RBF networks

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13th International Conference on Pattern Recognition, ICPR 1996

作者： Leonardis, Aleš Bischof, Horst Department for Pattern Recognition and Image Processing Technical University Vienna Treitlstraße 3/1832 A -1040 Vienna Austria University of Ljubljana Faculty of Computer and Information Science Computer Vision Laboratory Ljubljana Slovenia

ISBN: (纸本)081867282X

We propose an extension of RBF networks which includes a mechanism for optimizing the complexity of the network. The approach involves two procedures: adaptation (training) and selection. The first procedure adaptively changes the locations and the width of the centers of the basis functions and trains the linear weights. The selection procedure performs the elimination of some of the basis functions using an objective function. By iteratively combining these two procedures we achieve a controlled way of training and modifying RBF networks, which balances accuracy, learning time, and complexity of the resulting network. © 1996 IEEE.

关键词： Radial basis function networks

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Robust stereo on multiple resolutions

Robust stereo on multiple resolutions

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13th International Conference on Pattern Recognition, ICPR 1996

作者： Menard, Christian Leonardis, Aleš Department for Pattern Recognition and Image Processing Technical University Vienna Treitlstraße 3/1832 3A-1040 Vienna Austria University of Ljubljana Faculty of Computer and Information Science Computer Vision Laboratory Ljubljana Slovenia

ISBN: (纸本)081867282X

Stereo computation is one of the vision problems where the presence of outliers cannot be neglected. Most standard algorithms make unrealistic assumptions about noise distributions, which leads to erroneous results that cannot be corrected in subsequent postprocessing stages. In this paper we present a modification of the standard area-based correlation approach so that it can tolerate a significant number of outliers. The approach exhibits a robust behavior not only in the presence of mismatches but also in the case of depth discontinuities. The confidence measure of the correlation and the number of outliers provide two complementary sources of information which, when implemented in a multiresolution framework, result in a robust and efficient method. We present the results of this approach on a number of synthetic and real images. © 1996 IEEE.

关键词： Stereo image processing

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The EM/MPM algorithm for segmentation of textured images: analysis and further experimental results

The EM/MPM algorithm for segmentation of textured images: an...

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IEEE International Conference on image processing

作者： M.L. Comer E.J. Delp Computer Vision and Image Processing Laboratory School of Electrical Engineering Purdue University West Lafayette IN USA

In this paper we present new results relative to the "expectation-maximization/maximization of the posterior marginals" (EM/MPM) algorithm for simultaneous parameter estimation and segmentation of textured images. The goal of the EM/MPM algorithm is to minimize the expected value of the number of misclassified pixels. We present new theoretical results in this paper which show that the algorithm can be expected to achieve this goal, to the extent that the EM estimates of the model parameters are close to the true values of the model parameters. We also present new experimental results demonstrating the performance of the algorithm.

关键词： image segmentation image analysis image texture analysis Algorithm design and analysis Parameter estimation Pixel Stochastic processes computer vision image processing

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3D segmentation and labeling using self-organizing Kohonen network for volumetric measurements on brain CT imaging to quantify TBI recovery

3D segmentation and labeling using self-organizing Kohonen n...

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

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

In this paper, we present a new system to segment and label CT brain slices using a self-organizing Kohonen network. Our aim is to extract reliable and robust measures from CT images of Traumatic Brain Injury (TBI) patients that can accurately describe the morphological changes in the brain as recovery progresses. Segmentation is performed by assigning a feature pattern to each voxel, consisting of a scaled family of differential geometrical invariant features. The invariant feature pattern is input to Kohonen network for an unsupervised classification of the voxels into regions.

关键词： Labeling Volume measurement image segmentation Computed tomography Layout Electric variables measurement Engineering in Medicine and Biology Society Brain injuries Data mining Filters

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A watermark for digital images

A watermark for digital images

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IEEE International Conference on image processing

作者： R.B. Wolfgang E.J. Delp Computer Vision and Image Processing Laboratory Purdue Multimedia Testbed Laboratory School of Electrical Engineering Purdue University West Lafayette IN USA

The growth of networked multimedia systems has magnified the need for image copyright protection. One approach used to address this problem is to add an invisible structure to an image that can be used to seal or mark it. These structures are known as digital watermarks. We describe two techniques for the invisible marking of images. We analyze the robustness of the watermarks with respect to linear and nonlinear filtering, and JPEG compression. The results show that our watermarks detect all but the most minute changes to the image.

关键词： Watermarking Digital images Multimedia systems Copyright protection Seals Robustness Nonlinear filters Filtering Transform coding image coding

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Robust stereo on multiple resolutions

Robust stereo on multiple resolutions

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International Conference on Pattern Recognition

作者： C. Menard A. Leonardis Department for Pattern Recognition and Image Processing Technical University of of Vienna Vienna Austria Computer and InformationScience. Computer Vision Laboratory University of Ljubljana Ljubljana Slovenia

关键词： Stereo vision computer vision Noise robustness Information resources Correlation Pattern recognition image processing Layout Cameras Laboratories

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A Bayesian approach to error concealment in encoded video streams

A Bayesian approach to error concealment in encoded video st...

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IEEE International Conference on image processing

作者： P. Salama N. Shroff E.J. Delp Computer Vision and Image Processing Laboratory Purdue Multimedia Testbed Laboratory School of Electrical Engineering Purdue University West Lafayette IN USA Purdue University System West Lafayette IN US

In ATM networks cell loss causes data to be dropped in the channel. When digital video is transmitted over these networks one must be able to reconstruct the missing data so that the impact of these errors is minimized. In this paper we describe a Bayesian approach to conceal these errors. Assuming that the digital video has been encoded using the MPEG1 or MPEG2 compression scheme, each frame is modeled as a Markov random field. A maximum a posteriori estimate of the missing macroblocks and motion vectors is described based on the model.

关键词： Bayesian methods Streaming media Intelligent networks Laboratories Markov random fields Video sequences Interpolation image reconstruction Motion estimation computer errors

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Robust recovery of eigenimages in the presence of outliers and occlusions

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Journal of Computing and Information Technology 1996年第1期4卷 25-38页

作者： Leonardis, Aleš Bischof, Horst Department for Pattern Recognition and Image Processing Technical University Vienna Treitlstraße 3/1832 ViennaA-1040 Austria Computer Vision Laboratory Faculty of Computer and Information Science University of Ljubljana Tržaška 25 Ljubljana1000 Slovenia

The basic limitations of the current appearance-based matching methods using eigenimages are non-robust estimation of coefficients and inability to cope with problems related to occlusions and segmentation. In this paper we present a new approach which successfully solves these problems. The major novelty of our approach lies in the way how the coefficients of the eigenimages are determined. Instead of computing the coefficients by a projection of the data onto the eigenimages. we extract them by a hvpothesize-and-test paradigm using subsets of image points. Competing hypotheses arc then subject to a selection procedure based on the Minimum Description Length principle. The approach enables us not only lo reject outliers and to deal with occlusions but also to simultaneously use multiple classes of eigenimages.

关键词： Principal component analysis

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