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Signal and image processing strategies for reconstructing 3-D color flow maps from ultrasonic data

Signal and image processing strategies for reconstructing 3-...

引用

IEEE Symposium (IUS) Ultrasonics

作者： B.G. Zagar V.R. Algazi K. Whittaker Ferrara Center for Image Processing and Integrated Computing University of California Davis CA USA Riverside Research Institute New York NY USA

In this paper we present techniques for the 3-D reconstruction of ultrasonic images. In previous work, we presented methods for accurately determining the velocity profile within blood vessels along single lines of sight. Measurements of two dimensional velocity profiles in parallel planes can be achieved by simply translating the transducer over a rectangular grid. Doing so we obtain a well conditioned three-dimensional data set that can be used for a 3-D color flow mapping. In this paper we consider the problems of constructing a 3-D vessel structure and velocity vector field from image slices with good geometric quality. Examples of vessel structures and blood flow field rendering for both 7.5 MHz as well as 50 MHz transducer center frequencies are presented

关键词： image reconstruction image color analysis Blood vessels Multidimensional signal processing image resolution Acoustic signal processing Blood flow measurement Biomedical transducers Biomedical acoustic imaging Biomedical signal processing

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Analysis of texture images using robust fractal description

Analysis of texture images using robust fractal description

引用

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

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.< >

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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Comparison of lmage coding techniques with a picture quality scale 15

Comparison of lmage coding techniques with a picture quality...

引用

Applications of Digital image processing XV 1992

作者： Algazi, V.R. Kato, Y. Miyahara, M. Kotani CIPIC Center for Image Processing and Integrated Computing University of California DavisCA95616 United States CIPIC and MITSUBISHI Electric Corporation Kamakura Japan East Japan

A newly developed Picture Quality Scale (PQS) provides a numerical measure of image quality for monochrome images well correlated with the Mean Opinion Score. In this paper, we report some results on the evaluation and comparison of image coding methods with such an objective quality measure. The emphasis is given here to the evaluation of the quality of JPEG coding standard. We also review and discuss the important new areas of research that an objective distortion measure now makes possible. © 1993 SPIE. All rights reserved.

关键词： image coding

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Preprocessing network for multiple binary objects

Preprocessing network for multiple binary objects

引用

International Joint Conference on Neural Networks (IJCNN)

作者： S.D. You G.E. Ford Center for Image Processing Integrated Computing University of California Davis CA USA

This paper describes a simple network for a selective attention mechanism for multiple binary objects. The network is based on a concept similar to the region growing approach and is able to focus attention on one object at a time from multiple objects for a recognition process. The network consists of a growing network and an attention network. The growing network focuses on one object among others; while the attention network provides the "seed" for the region network to grow. Finally, a modification for "weightless" implementation is suggested.

关键词： Signal processing image processing Computer networks Neural networks Object recognition image recognition image converters image segmentation Differential equations

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Structure preserving inhomogeneous diffusion in image filtering

Structure preserving inhomogeneous diffusion in image filter...

引用

Asilomar Conference on Signals, Systems & Computers

作者： A.I. El-Fallah G.E. Ford CIPIC Center for Image Processing and Integrated Computing University of California Davis Davis CA USA CIPIC Center for Image Processing and Integrated Computing University of California슠Davis Davis CA USA

We introduce a new theory relating the magnitude of the image surface normal to an inhomogeneous diffusion that solely diffuses (averages) the mean curvature of the image surface. We discuss the remarkable properties of this diffusion stressing the regularity it imposes on regions and boundaries while preserving the locality of edges and lines. Experiments demonstrating the excellent performance of the algorithms in the areas of noise removal and enhancement are presented.< >

关键词： Filtering

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image filtering by gradient inverse inhomogeneous diffusion

Image filtering by gradient inverse inhomogeneous diffusion

引用

1993 IEEE International Conference on Acoustics, Speech, and Signal processing

作者： A.I. El-Fallah G.E. Ford CIPIC Center for Image Processing and Integrated Computing University of California Davis CA USA

A method is developed for the synthesis of a nonlinear adaptive filter based on solutions to the inhomogeneous diffusion equation. The approach is based on the specification of the first derivative of the signal in time (scale). A general solution is derived and is then specialized to the scale invariance case, in which the diffusion coefficient is shown to be the gradient inverse. A novel discrete realization of the inhomogeneous diffusion equation is developed for the noise removal problem, and experimental results are shown. The proposed algorithm not only removes noise but simultaneously enhances and localizes edges. It is extremely simple and parallel, and does not require the detection of any of the many possible line and edge configurations. Since the algorithm is sensitive to the local context, it satisfies human vision requirements more than conventional methods which rely on minimizing the mean square error.< >

关键词： Filtering image processing Signal synthesis Adaptive filters Ear Nonlinear equations Smoothing methods Spatial resolution Taylor series

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Network model for invariant object recognition and rotation angle estimation

Network model for invariant object recognition and rotation ...

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International Joint Conference on Neural Networks (IJCNN)

作者： S.D. You G.E. Ford Center for Image Processing and Integrated Computing University of California Davis CA USA University of California Davis Davis CA US

This paper proposes a network architecture for invariant object recognition and rotation angle estimation. The model has four stages. The first stage is a network implementation of the Radon transform, which is used to separate rotation and translation of the input object into translations on the /spl theta/-axis and s-axis, respectively. The second stage provides translation-invariant features using correlations and a maximum-pick-up network. The outputs of this stage are used both for object recognition and rotation angle estimation. The recognition stage employs a Rapid transform for rotation invariance and a multilayer feedforward network for recognition. The estimation stage consists of several feature templates obtained from exemplars. The best fit among all templates determines the rotation angle of the input object. The overall complexity of the weight connection of the network is O(N/sup 3/) for N/spl times/N pixels, which is lower than that of several established networks. We test our network using a set of printed numerical characters.

关键词： Object recognition Nonhomogeneous media image recognition Discrete transforms image processing Computer networks Computer architecture Testing Neural networks Object detection

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SPACE SCALE ANALYSIS FOR image SAMPLING AND INTERPOLATION

SPACE SCALE ANALYSIS FOR IMAGE SAMPLING AND INTERPOLATION

引用

INTERNATIONAL CONF ON ACOUSTICS, SPEECH AND SIGNAL processing

作者： FORD, GE ESTES, RR CHEN, H CIPIC Center for Image Processing and Integrated Computing University of California Davis Davis 95616 CA United States

ISBN: (纸本)0780305329

In image processing operations involving changes in the sampling grid, including increases or decreases in resolution, care must be taken to preserve image structure. Structure includes regions of high contrast, such as edges, streaks, or corners, all indicated by large gradients. In this paper, we consider the use of local spatial analysis for both sampling and interpolation. Anisotropic diffusion is considered as a directional smoothing technique that preserves structure. This is used in conjunction with a method of directional interpolation, which is also based on structure analysis. © 1992 IEEE.

关键词： Interpolation

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Perceptually based coding of monochrome and color still images

Perceptually based coding of monochrome and color still imag...

引用

1992 Data Compression Conference, DCC 1992

作者： Reed, T.R. Algazi, V.R. Ford, G.E. Hussain, I. CIPIC Center for Image Processing and Integrated Computing University of California DavisCA United States

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Object Recognition Based On Projection

Object Recognition Based On Projection

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1992 International Joint Conference on Neural Networks, IJCNN 1992

作者： You, Shingchern D. Ford, Gary E. Center for Image Processing and Integrated Computing University of California Davis DavisCA95616 United States

ISBN: (纸本)0780305590

This paper proposes a new connectionist model for invariant object recognition. The model has four stages. The first stage obtains projections from the input image plane. The projection features can separate rotation and translation of the input object to translations on the ? axis and s axis, respectively. The second stage adopts the Mellin transform to provide scale invariance. The outputs of this stage are translation and scale invariant. To obtain rotation invariance, the Rapid transform is employed. Finally, a pattern classification network is used for recognition. In our experiments, we used five exemplars to recognize 240 transformed test objects and very good results were obtained. © 1992 IEEE.

关键词： Object recognition

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