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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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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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On div-curl regularization for motion estimation in 3-D volumetric imaging

On div-curl regularization for motion estimation in 3-D volu...

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IEEE International Conference on image Processing

作者： S.N. Gupta J.L. Prince Image Analysis and Communications Laboratory Department of Electrical and Computer Engineering Johns Hopkins University Baltimore MD USA

We consider the classical optical flow algorithm due to Horn and Schunck (1981) for estimating the motion of brightness patterns between image pairs. We use a modified smoothness condition based on the divergence and curl of the velocity field. In previous work, we have developed well-posed stochastic state-space models for these optical flow methods in two dimensions. This paper extends our results to 3-D. We first show that by using the first order div-curl spline, it is not possible to obtain a first order linear differential well-posed model in 3-D. Next, we employ the second order div-curl spline smoothness condition and develop well-posed state-space models.

关键词： Motion estimation Brightness image motion analysis Spline Stochastic processes Optical imaging Optical computing State-space methods image sequence analysis Laboratories

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Experiments in multiresolution motion estimation for multifrequency tagged cardiac MR images

Experiments in multiresolution motion estimation for multifr...

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IEEE International Conference on image Processing

作者： S. Androutsellis-Theotokis J.L. Prince Image Analysis and Communications Laboratory Department of Electrical and Computer Engineering Johns Hopkins University Baltimore MD USA

In tagged magnetic resonance imaging of the human heart, different frequency patterns can be imposed and imaged as the heart moves. It is advantageous to use low-frequency patterns to improve both image acquisition time and the performance of gradient-based optical flow motion analysis methods. This paper explores the effects of three factors-image prefiltering, multiresolution optical flow, and multifrequency tag patterns through a serious of controlled simulation experiments. We show that use of image prefiltering in a multiresolution optical flow approach achieves significant performance gains, and permits a factor of four reduction in imaging time, without the need for multifrequency tag patterns.

关键词： Motion estimation image motion analysis Heart Magnetic resonance imaging Humans Frequency Motion analysis Optical control image resolution Performance gain

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Thresholding methods considering the quantization error of an image

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SYSTEMS AND computerS IN JAPAN 1996年第9期27卷 63-71页

作者： Sekita, I Kurita, T Otsu, N Abdelmalek, NN Electrotechnical Laboratory Tsukuba Japan 305 Ottawa Ontario Canada Nabih Nessim Abdelmalek:received his B.S. degree in Electrical Engineering in 1951 and his B.S. degree in Mathematics in 1954 both from Cairo University Egypt and his Ph.D. degree in Theoretical Nuclear Physics in 1958 from Manchester University U.K. After 7 years in the Department of Mathematics on the Faculty of Engineering Cairo University he moved to Canada and became a member of the scientific staff at Bell Northern Research in Ottawa. After a period of 2 years he joined the National Research Council of Canada in Ottawa. His current interest is the application of numerical analysis techniques to the problem of image processing particularly image enhancement and restoration data compression pattern classification parameter estimation and 3D robot vision.

Thresholding techniques are fundamental for region segmentation of a gray level image. It is often realistic to assume that gray pixel levels are subject to a mixture of normal distributions. This paper points out problems caused by the underestimation of variances from a frequency distribution and solves the problems by transforming a frequency distribution into a piecewise-continuous distribution based on the maximum entropy criterion. This transformation improves conventional thresholding techniques. Experimental results showed the effectiveness of the transformation.

关键词： threshold quantization mixture of normal distributions frequency distribution histogram

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Texture segmentation using hierarchical wavelet decomposition

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PATTERN RECOGNITION 1995年第12期28卷 1819-1824页

作者： Salari, E Ling, Z Computer Vision and Image Processing Laboratory Department of Electrical Engineering University of Toledo Toledo OH 43606 U.S.A.

This paper presents a texture segmentation algorithm based on a hierarchical wavelet decomposition. Using Daubechies' four-tap filter, an original image is decomposed into three detail images and one approximate image. The decomposition can be recursively applied to the approximate image to generate a lower resolution of the pyramid. The segmentation starts at the lowest resolution using the K-means clustering scheme and textural features obtained from various sub-bands. The result of segmentation is propagated through the pyramid to a higher resolution with continuously improving the segmentation. The lower resolution levels help to build the contour of the segmented texture, while higher levels refine the process, and correct possible errors.

关键词： Texture segmentation Wavelet transform Clustering algorithm Multiresolution segmentation

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Searching for patterns in remote sensing image databases using neural networks

Searching for patterns in remote sensing image databases usi...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： J.D. Paola R.A. Schowengerdt Digital Image Analysis Laboratory Department of Electrical and Computer Engineering University of Arizona Tucson Tucson AZ USA

The authors have investigated a method, based on a successful neural network multispectral image classification system, of searching for single patterns in remote sensing databases. While defining the pattern to search for and the feature to be used for that search (spectral, spatial, temporal, etc.) is challenging, a more difficult task is selecting competing patterns to train against the desired pattern. Schemes for competing pattern selection, including random selection and human interpreted selection, are discussed in the context of an example detection of dense urban areas in Landsat Thematic Mapper imagery. When applying the search to multiple images, a simple normalization method can alleviate the problem of inconsistent image calibration. Another potential problem, that of highly compressed data, was found to have a minimal effect on the ability to detect the desired pattern. The neural network algorithm has been implemented using the PVM (Parallel Virtual Machine) library and nearly-optimal speedups have been obtained that help alleviate the long process of searching through imagery.

关键词： Remote sensing image databases Neural networks Multispectral imaging Spatial databases Humans Urban areas Satellites Calibration image coding

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The effect of lossy image compression on image classification

The effect of lossy image compression on image classificatio...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： J.D. Paola R.A. Schowengerdt Digital Image Analysis Laboratory Department of Electrical and Computer Engineering University of Arizona Tucson Tucson AZ USA

The authors have classified four different images, under various levels of JPEG compression, using the following classification algorithms: minimum-distance, maximum-likelihood, and neural network. The training site accuracy and percent difference from the original classification were tabulated for each image compression level, with maximum-likelihood showing the poorest results. In general, as compression ratio increased, the classification retained its overall appearance, but much of the pixel-to-pixel detail was eliminated. The authors also examined the effect of compression on spatial pattern detection using a neural network.

关键词： image coding image classification Transform coding Neural networks Discrete cosine transforms Remote sensing Classification algorithms image storage Discrete transforms Quantization

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On balanced realizations of 2-D delta-operator formulated discrete-time systems

On balanced realizations of 2-D delta-operator formulated di...

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Southcon

作者： K. Premaratne M.M. Ekanayake P.H. Bauer Department of Electrical and Computer Engineering University of Miami Coral Gables FL USA Department of Electrical Engineering Laboratory of Image and Signal Analysis University of Notre Dame Notre Dame IN USA

Delta-operator based implementations can avoid the numerical ill-conditioning usually associated with the high speed shift-operator based implementations of discrete-time systems. Moreover, it provides a unified methodology for tackling both continuous- and discrete-time systems. In particular, it has been shown that, delta-operator based balanced realizations can offer superior coefficient sensitivity properties under fixed-point arithmetic. The authors address computation of balanced realizations. For this purpose, given a discrete-time system, the relationship between its shift- and delta-operator formulated balanced realizations is presented.

关键词： Fixed-point arithmetic Computational modeling Sampling methods Polynomials Laboratories Signal analysis Computed tomography Eigenvalues and eigenfunctions Gold Stability

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