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

作者： E. Rignot R. Chellappa Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA Signal and Image Processing Institute Department of Electrical Engineering-Systems University of Southern California Los Angeles CA USA

Several algorithms for segmenting multifrequency synthetic aperture radar (SAR) complex data into regions of similar and homogeneous backscattering characteristics are presented. The image model is composed of two models, one for the multifrequency complex amplitudes (i.e., speckle), and the other for the region labels. The speckle model is derived from SAR physics. The corresponding analysis illustrates the importance of having a good knowledge of the characteristics of the SAR imaging and processing systems to correctly model the high order statistics of speckle. The region model, on the other hand, uses a multilevel Ising model (a Markov random field) to represent the grouping of pixels into regions. The two models are combined using Bayes' rule to define an optimal region labeling of the scene given the multifrequency complex amplitudes. Two alternatives are presented that can be implemented on an optimization network. The performance of the segmentation technique is illustrated.< >

关键词： Speckle image segmentation Synthetic aperture radar Backscatter Physics image analysis Radar polarimetry Statistical analysis Markov random fields Labeling

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Classification of Multifrequency Multilook Synthetic Aperture Radar Data

Classification of Multifrequency Multilook Synthetic Apertur...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： E. Rignot R. Chellappa R. Kwok Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA Signal and Image Processing Institute Department of Electrical Engineering-Systems University of Southern California Los Angeles CA USA

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Real-time high-ratio image compression using adaptive VLSI neuroprocessors

Real-time high-ratio image compression using adaptive VLSI n...

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

作者： B.J. Sheu W.-C. Fang Department of Electrical Engineering Signal and Image Processing Institute University of Southern California Los Angeles CA USA Jet Propulsion Laboratory Advanced Computer Systems and Technology Section California Institute of Technology Pasadena CA USA

An adaptive VLSI neuroprocessor based on vector quantization algorithm has been developed for real-time high-ratio image compression applications. This VLSI neural-network-based vector quantization (NNVQ) module combines a fully parallel vector quantizer with a pipelined codebook generator for a broad area of data compression applications. The NNVQ module is capable of producing good-quality reconstructed data at high compression ratios more than 20. The vector quantizer chip has been designed, fabricated, and tested. It contains 64 inner-product neural units and a high-speed extendable winner-take-all block. This mixed-signal chip occupies a compact silicon area of 4.6*6.8 mm/sup 2/ in a 2.0- mu m scalable CMOS technology. The throughput rate of the 2- mu m NNVQ module is 2 million vectors per second and its equivalent computation power is 3.33 billion connections per second.< >

关键词： image coding Very large scale integration Vector quantization Neural networks Data compression High performance computing Clustering algorithms Application software image reconstruction CMOS technology

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A real-time VLSI neuroprocessor for adaptive image compression based upon frequency-sensitive competitive learning

A real-time VLSI neuroprocessor for adaptive image compressi...

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International Joint Conference on Neural Networks (IJCNN)

作者： W.-C. Fang B.J. Sheu O.T.-C. Chen Jet Propulsion Laboratory Advanced Computer Systems and Technology Section California Institute of Technology Pasadena CA USA Department of Electrical Engineering Signal and Image Processing Institute University of Southern California Los Angeles CA USA

The frequency-sensitive competitive learning (FSCL) algorithm and its associated VLSI neuroprocessor have been developed for adaptive vector quantisation (AVQ). Simulation results show that the FSCL algorithm is capable of producing a good-quality codebook for AVQ at high compression ratios of more than 20 in real time. This VLSI neural-network-based vector quantization design includes a fully parallel vector quantizer and a pipelined codebook generator to provide an effective data compression scheme. It provides a computing capability as high as 3.33 billion connections per second. Its performance can achieve a speedup of 750 compared with SUN-3/60 and a compression ratio of 33 at a signal-to-noise ratio of 23.81 dB.< >

关键词： Very large scale integration image coding Frequency Neural networks Power capacitors Vector quantization CMOS technology image reconstruction Speech coding Data compression

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Machine vision in the 1990s: Applications and how to get there

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Machine vision and Applications 1991年第2期4卷 113-126页

作者： Petkovic, D. Wilder, J. Ejiri, Masakazu Haralick, Robert M. Jain, Ramesh Ruetz, Peter A. Sklansky, Jack Swonger, C.W. IBM Almaden Research Center San Jose California United States Rutgers University New Brunswick New Jersey United States Central Research Laboratory Hitachi Ltd. Tokyo 185 Kokubunji Japan Department of Electrical Engineering University of Washington Seattle Washington United States Artificial Intelligence Laboratory The University of Michigan Ann Arbor Michigan United States LSI LOGIC Corporation Milpitas California United States University of California Irvine California United States Image Processing Systems Division Environmental Research Institute of Michigan (ERIM) Ann Arbor United States

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Electromagnetic imaging of dynamic brain activity

Electromagnetic imaging of dynamic brain activity

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： J. Mosher P. Lewis J. Lewine J. George R. Leahy M. Singh Univ. of Southern California Los Angeles CA USA Mechanical & Electronic Engineering Division Los Alamos National Laboratory Los Alamos NM USA Signal & Image Processing Institute Department Electrical Engineering-Systems University of Southern California Los Angeles CA USA Department of Radiology University of Southern California Los Alamos CA USA

Neural activity in the brain produces weak dynamic electromagnetic fields that can be measured by an array of sensors. Using a spatiotemporal modeling framework, a novel approach to localization of multiple neural sources has been developed. This approach is based on the MUSIC algorithm originally developed for estimating the direction of arrival of signals impinging on a sensor array. The authors present applications of this technique to magnetic field measurements of a phantom and of a human evoked somatosensory response. The results of the somatosensory localization are mapped onto the brain anatomy obtained from magnetic resonance images.< >

关键词： Brain Sensor arrays Magnetic field measurement Magnetic sensors Electromagnetic fields Electromagnetic measurements Spatiotemporal phenomena Multiple signal classification Direction of arrival estimation Imaging phantoms

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SEGMENTATION OF SYNTHETIC APERTURE RADAR COMPLEX DATA

SEGMENTATION OF SYNTHETIC APERTURE RADAR COMPLEX DATA

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Asilomar Conference on Signals, systems & Computers

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Spatial localization of neural sources using the magnetoencephalogram

Spatial localization of neural sources using the magnetoence...

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Annual ASSP Workshop on Spectrum Estimation and Modeling

作者： J.C. Mosher P.S. Lewis R. Leahy TRW Systems Engineering & Development Division Redondo Beach CA USA Signal and Image Processing Institute Department of Electrical Engineering-Systems University of Southern California Los Angeles CA USA Los Alamos National Laboratory Mechanical & Electronic Engineering MEE-3 MS 5580 Los Alamos NM USA

An array of superconducting quantum interference device (SQUID) biomagnetometers may be used to measure the spatio-temporal neuromagnetic field or magnetoencephalogram (MEG) produced by the brain in response to a given sensory stimulus. A popular model for the neural activity that produces these fields is a set of current dipoles. It is assumed that the location, orientation, and magnitude of the dipoles are unknown. The authors show how the problem may be decomposed into the estimation of the dipole locations using nonlinear minimization followed by linear estimation of the associated moment time series. The methods described are demonstrated in a simulated application to a three dipole problem. Cramer-Rao lower bounds are derived for the white Gaussian noise case.< >

关键词： Magnetic field measurement SQUIDs Inverse problems Superconducting magnets Laboratories Sensor arrays Gaussian noise image processing systems engineering and theory Extraterrestrial measurements

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PC-based floating point imaging workstation

PC-based floating point imaging workstation

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Imaging Workstations 1989

作者： Guzak, C.J. Pier, R.M. Chinn, P. Kim, Y. Image Processing Systems Laboratory Department of Electrical Engineering University of Washington SeattleWA98195 United States

The medical, military, scientific and industrial communities have come to rely on imaging and computer graphics for solutions to many types of problems. systems based on imaging technology are used to acquire and process images, and analyze and extract data from images that would otherwise be of little use. images can be transformed and enhanced to reveal detail and meaning that would go undetected without imaging techniques. The success of imaging has increased the demand for faster and less expensive imaging systems and as these systems become available, more and more applications are discovered and more demands are made. From the designer's perspective the challenge to meet these demands forces him to attack the problem of imaging from a different perspective. The computing demands of imaging algorithms must be balanced against the desire for affordability and flexibility. systems must be flexible and easy to use, ready for current applications but at the same time anticipating new, unthought of uses. Here at the University of Washington image processing systems Lab (IPSL) we are focusing our attention on imaging and graphics systems that implement imaging algorithms for use in an interactive environment. We have developed a PC -based imaging workstation with the goal to provide powerful and flexible, floating point processing capabilities, along with graphics functions in an affordable package suitable for diverse environments and many applications. © 1989 SPIE. All rights reserved.

关键词： Digital arithmetic

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image segmentation using human visual system properties with applications in image compression

Image segmentation using human visual system properties with...

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Human vision, Visual processing, and Digital Display 1989

作者： Peterson, Heidi A. Rajala, Sarah A. Delp, Edward J. Computer Vision and Image Processing Laboratory School of Electrical Engineering Purdue University West LafayetteIN47907 United States Department of Electrical and Computer Engineering North Carolina State University RaleighNC27695 United States

Many image compression techniques involve segmentation of a gray level image. With such techniques, information is extracted that describes the regions in the segmented image, and this information is then used to form a coded version of the image. In this paper we present a region-growing-based segmentation technique that incorporates human visual system properties, and describe the use of this technique in image compression. We also discuss the effect of requantizing a segmented image. Requantization of a segmented image is useful because it can lead to a reduction in the number of bits required to code the description of the regions in the segmented image. This results in a lower data rate. We show that the number of gray levels in a segmented image can be reduced by a factor of at least twelve, without noticeable degradation in the quality of the segmented image. This result is attributable to human visual system properties having to do with contrast sensitivity, and to the fact that requantization of a segmented image does not usually reduce significantly the number of distinct segments in the image. In addition, in this paper we explore the relationship between the number of segments in an image, and the extent of requantization possible before noticeable degradation occurs in the image. Finally, we discuss the impact of the above results on image compression algorithms, and present some experimental results. © 1989 SPIE. All rights reserved.

关键词： image compression

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