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检索条件"机构=Computer Vision and Image Analysis"

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Constrained optimization in pattern classification using ART

Constrained optimization in pattern classification using ART

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International Joint Conference on Neural Networks (IJCNN)

作者： Shuyu Yang S. Mitra Department of Electrical and Computer Engineering Computer Vision & Image Analysis Laboratory Texas Tech University TX USA

Pattern classification problems involve constrained optimization in the form of minimization of chosen cost functions. Such embedded constraints in integrated neural-fuzzy pattern recognition systems improve the performance of these systems. The comparative performance of an ART-based pattern classifier integrated with fuzzy optimization constraints is demonstrated in designing vector quantizers.

关键词： Constraint optimization Pattern classification Subspace constraints Clustering algorithms Vector quantization Algorithm design and analysis Pattern recognition Neural networks Fuzzy logic Testing

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Designing 3-D nonlinear diffusion filters for high performance cluster computing

Designing 3-D nonlinear diffusion filters for high performan...

引用

24th German Association for Pattern Recognition Symposium, DAGM 2002

作者： Bruhn, Andrés Jakob, Tobias Fischer, Markus Kohlberger, Timo Weickert, Joachim Brüning, Ulrich Schnörr, Christoph Mathematical Image Analysis Group Mathematics and Computer Science Saarland University Building 27.1 Saarbrücken66041 Germany Computer Architecture Group Department of Mathematics and Computer Science University of Mannheim Mannheim68131 Germany Computer Vision Graphics and Pattern Recognition Group Department of Mathematics and Computer Science University of Mannheim Mannheim68131 Germany

ISBN: (纸本)354044209X

This paper deals with parallelization and implementation aspects of PDE based image processing models for large cluster environments with distributed memory. As an example we focus on nonlinear isotropic diffusion filtering which we discretize by means of an additive operator splitting (AOS). We start by decomposing the algorithm into small modules that shall be parallelized separately. For this purpose image partitioning strategies are discussed and their impact on the communication pattern and volume is analyzed. Based on the results we develop an algorithmic implementation with excellent scaling properties on massively connected low latency networks. Test runs on a high–end Myrinet cluster yield almost linear speedup factors up to 209 for 256 processors. This results in typical denoising times of 0.5 seconds for five iterations on a 256 × 256 × 128 data cube. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Cluster computing

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Rate distortion in image coding from embedded optimization constraints in vector quantization

Rate distortion in image coding from embedded optimization c...

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International Joint Conference on Neural Networks (IJCNN)

作者： Shuyu Yang S. Mitra Computer Vision & Image Analysis Laboratory Department of Electrical and Computer Engineering Texas Tech University Lubbock TX USA

ISBN: (纸本)0780370449

We compared the performance of two recently developed vector quantization algorithms using different optimization criteria for clustering, namely, the adaptive fuzzy leader clustering, a neuro-fuzzy algorithm, and the deterministic annealing, another unsupervised clustering algorithm based on probabilistic and statistical physics frameworks, with the rate distortion criterion as a performance measure. Such a comparison is useful for evaluating the efficiency of clustering algorithms for the purpose of image vector quantization instead of the conventional misclassification evaluation. This method is extended from the analysis of image coding in a spatial domain to sample vectors in the wavelet domain with predictable distribution. These sample vectors possess a multidimensional generalized Gaussian distribution through the new multi-scale feature extraction method. Our preliminary results show much improvement on the reconstructed image quality over JPEG.

关键词： Rate-distortion image coding Clustering algorithms Vector quantization Annealing Physics Distortion measurement image analysis Wavelet analysis Wavelet domain

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An adaptive deterministic annealing approach for medical image segmentation

An adaptive deterministic annealing approach for medical ima...

引用

Conference of the North American Fuzzy Information Processing Society - NAFIPS

作者： S. Mitra R. Castellanos S. Joshi Computer Vision and Image Analysis Laboratory Department of Electrical and Computer Engineering Texas Tech University Lubbock TX USA

We present a stochastic model based technique that uses the concept of deterministic annealing to obtain a generalized solution to the nonconvex optimization problem encountered by many image segmentation techniques. Deterministic annealing [DA] is an elegant and useful tool for clustering and classification. This novel optimization approach works with the efficiency of a deterministic procedure and has been successfully applied to a number of combinatorial optimization problems. We demonstrate effective segmentation of simulated MR brain images and provide a quality measure for accuracy of classification. A generalized deterministic annealing procedure, which works tender a structural constraint of mass or density, has been utilized for this purpose. This method produces a hierarchy of solutions giving segmentation results from a coarse to a fine level. Automatic edge detection can be performed using these solutions that are at different degrees of coarseness. The procedure has been made more efficient by utilizing a new similarity parameter from the concepts of neuro-fuzzy clustering.

关键词： Annealing Biomedical imaging image segmentation Brain modeling Clustering algorithms image edge detection Lagrangian functions computer vision Laboratories Stochastic processes

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Efficient coding by neuro-fuzzy clustering in vector quantization of wavelet decomposed signals

Efficient coding by neuro-fuzzy clustering in vector quantiz...

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Conference of the North American Fuzzy Information Processing Society - NAFIPS

作者： S. Mitra S. Pemmaraju Computer Vision and Image Analysis Laboratory Department of Electrical Engineering Texas Tech University Lubbock TX USA

Multiresolution representation of wavelets in image decomposition and coding shows potential of developing an efficient image compression technique with minimum distortion when vector quantization (VQ) is used. This paper presents a multiresolution and adaptive approach to VQ codebook generation by employing a fuzzy distortion measure embedded in a self-organizing neural network ensuring fast convergence and minimum distortion. Multiresolution codebooks are generated for the wavelet decomposed images using neuro-fuzzy clustering algorithms resulting in significant improvement in the coding process. The signal transformation and vector quantization stages together yield at least, 64:1 bit rate reduction with good visual quality and acceptable peak signal to noise ratio and mean square error. The performance of this new VQ coding technique has been compared to that of the well-known Linde, Buzo, and Gray-VQ for a variety of image classes. In each case, the new VQ technique demonstrated superior ability for fast convergence with minimum distortion at similar bit rate reduction than the existing VQ techniques.

关键词： Vector quantization image coding Signal resolution image resolution Distortion measurement Convergence Bit rate image decomposition Fuzzy neural networks Neural networks

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Efficient image coding using multiresolution wavelet transform and vector quantization

Efficient image coding using multiresolution wavelet transfo...

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IEEE Southwest Symposium on image analysis and Interpretation

作者： S. Pemmaraju S. Mitra Computer Vision and Image Analysis Laboratory Department of Electrical Engineering Texas Tech University Lubbock TX USA

image compression forms the backbone for several applications such as storage of images in a database, picture archiving, TV and facsimile transmission, and video conferencing. Compression of images involves taking advantage of the redundancy in data present within an image. A fundamental goal of image compression is to reduce the bit rate for transmission and storage while maintaining an acceptable fidelity or image quality. Existing VQ algorithms however, suffer from a number of practical problems, e.g. codebook initialization, long search process, and getting trapped in local minima. This paper presents an adaptive vector quantization algorithm which uses a neuro-fuzzy clustering technique for optimizing the distortion measure. The fuzzy approach forms the basis for accurately optimizing each codevector by determining the fuzzy centroid of each class. In addition, a multiresolution wavelet decomposition scheme is adopted to make the image better suited for compression and to enable its progressive transmission.

关键词： image coding image resolution Wavelet transforms image storage Clustering algorithms Spine image databases TV Facsimile Videoconference

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Bounds on the optimal elasticity parameters for a snake 8th

引用

8th International Conference on image analysis and Processing, ICIAP 1995

作者： Larsen, Ole V. Radeva, Petia Martí, Enric Laboratory of Image Analysis Aalborg University Denmark Computer Vision Center Autonomous University of Barcelona Spain

ISBN: (纸本)3540602984

This paper develops a formalism by which an estimate for the upper and lower bounds for the elasticity parameters for a snake can be obtained. Objects different in size and shape give rise to different bounds. The bounds can be obtained based on an analysis of the shape of the object of interest. Experiments on synthetic images show a good correlation between the estimated behaviour of the snake and the one actually observed. Experiments on real X-ray images show that the parameters for optimal segmentation lie within the estimated bounds. © Springer-Verlag Berlin Heidelberg 1995.

关键词： image segmentation

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New morphological operators: BERD and BDRE

New morphological operators: BERD and BDRE

引用

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

作者： Kher, A. Mitra, S. Computer Vision and Image Analysis Laboratory Dept. of Electr. Eng. Texas Tech. Univ. Lubbock TX79409 United States

ISBN: (纸本)0818662506

The authors briefly describe their new morphological operators of »boundary erosion region dilation (BERD)» and »boundary dilation region erosion (BDRE)». The paper covers definitions, properties, and some of the applications of these operators. BERD and BDRE operations may be used to approximately perform the job of various common morphological filtering operations with lesser computational times. BERD and BDRE operations with 2-D structuring elements may be used to construct robust connectivity preserving filters. Unlike commonly used median closing and opening operators/sup 1,5,6/, these filters do not destroy thin regions which rapidly change their direction or the regions which can not be probed by straight lines in limited directions. They have used these connectivity preserving filters for removing speckle noise from SAR images while retaining thin regions and fine details of regions boundaries. © 1994 IEEE.

关键词： Mathematical morphology

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analysis of texture images using robust fractal description

Analysis of texture images using robust fractal description

引用

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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New morphological operators: BERD and BDRE

New morphological operators: BERD and BDRE

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IEEE Southwest Symposium on image analysis and Interpretation

作者： A. Kher S. Mitra Computer Vision and Image Analysis Laboratory Department of Electrical Engineering Texas Tech University Lubbock TX USA

The authors briefly describe their new morphological operators of "boundary erosion region dilation (BERD)" and "boundary dilation region erosion (BDRE)". The paper covers definitions, properties, and some of the applications of these operators. BERD and BDRE operations may be used to approximately perform the job of various common morphological filtering operations with lesser computational times. BERD and BDRE operations with 2-D structuring elements may be used to construct robust connectivity preserving filters. Unlike commonly used median closing and opening operators/sup 1,5,6/, these filters do not destroy thin regions which rapidly change their direction or the regions which can not be probed by straight lines in limited directions. They have used these connectivity preserving filters for removing speckle noise from SAR images while retaining thin regions and fine details of regions boundaries.< >

关键词： Morphological operations Filters image analysis image processing Filtering Morphology Pixel computer vision Laboratories Noise robustness

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