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检索条件"机构=Analogical and Neural Computing Laboratory of the Computer"

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Optimal CNN templates for deconvolution

Optimal CNN templates for deconvolution

引用

IEEE International Workshop on Cellular Nanoscale Networks and their Applications, (CNNA)

作者： L. Orzo Analogical & Neural Computing Systems Laboratory at the Computer and Automation Research Institute of the Hungarian Academy of Sciences Budapest Hungary

A simplified version of the gradient descent method is introduced as a straightforward way to find optimal 3/spl times/3 CNN templates for the inversion of known point spread functions (PSF). In practical applications the determination of this inverse is necessary to fulfil deconvolution tasks. The proposed method is much faster than the previously applied algorithms (like genetic algorithm) and still, in almost all practically important cases, it is convergent. Moreover, unlike a closed form method, it leads to 3/spl times/3 templates instead of 5/spl times/5 or bigger ones. In several important practical cases the PSF, which can be caused by motion, out of focus or the aberration of the imaging system, can be computed from object positions and from the optical system's parameters. Iterative deconvolution algorithms, which are necessary for volume reconstruction from microscopic image sequences, require considerable computation time. Using CNN-UM chips for these deconvolution tasks, a much higher speed, even real time processing seems to be achievable.

关键词： Cellular neural networks Deconvolution Genetic algorithms Focusing Optical imaging Optical computing Iterative algorithms Image reconstruction Microscopy Image sequences

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The implementation of a nonlinear wave metric for image analysis and classification on the 64/spl times/64 I/O CNN-UM chip

The implementation of a nonlinear wave metric for image anal...

引用

IEEE International Workshop on Cellular Nanoscale Networks and their Applications, (CNNA)

作者： I. Szatmari Analogical and Neural Computing Laboratory. Computer and Automation institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

In this paper the implementation of a nonlinear wave metric on the 64/spl times/64 I/O CNN-UM chip and its experimental results are presented. The nonlinear wave metric was designed and introduced as a generalized theorem for object analysis and classification. This proposed metric includes the well-known distance measures such as Hamming, Hausdorff metrics as special cases. The defined computational method is well-suited for cellular neural network (CNN) architecture and the experimental results shows good correlation with theoretic considerations.

关键词： Image analysis Hamming distance Semiconductor device measurement Cellular neural networks computer networks computer architecture Image recognition Laboratories Automation Cellular networks

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20 /spl mu/sec focal plane image processing

20 /spl mu/sec focal plane image processing

引用

IEEE International Workshop on Cellular Nanoscale Networks and their Applications, (CNNA)

作者： A. Zarandy M. Csapodi T. Roska Analogical and Neural Computing Laboratory of the Computer and Automation Research Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

Ultra high frame rate image processing was achieved by applying CNN-UM (cellular neural net-Universal Machine) chips as focal plane array processors. By applying parallel optical input, and reading out binary decision from the chip only the computational overhead is negligible. This makes possible even 50,000 fps image capturing and complex processing. Experiments were done and are described in the paper.

关键词： Image processing Cellular neural networks Optical computing Optical sensors Hardware System testing Semiconductor device measurement Cameras Laboratories Turing machines

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An optical CNN implementation with stored programmability

An optical CNN implementation with stored programmability

引用

IEEE International Symposium on Circuits and Systems (ISCAS)

作者： S. Tokes L. Orzo C. Rekeczky T. Roska A. Zarandy Analogical & Neural Computing Systems Laboratory at the Computer and Automation Research Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

The objective of this paper is to provide a framework for the implementation of programmable Opto-Electronic Analogic CNN (POAC) computers embedding CNN Universal Chips. Specifically, a new method for optical CNN implementation is provided and some details are experimentally studied. The POAC architecture includes the integration of an optical processing system, such as a joint transform correlator, with the fast spatio-temporal processing capabilities of a CNN-UM chip. We have built and tested an optical sub-unit of this experimental optoelectronic architecture to examine their processing capabilities for complex target recognition tasks. Preliminary result of these measurements will also be presented. The main idea is to introduce stored programmability into optical computing.

关键词： Cellular neural networks Nonlinear optics Optical filters Optical network units Correlators computer architecture Embedded computing Optical computing Image processing Target recognition

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A qualitative model-framework for spatio-temporal effects in vertebrate retinas

A qualitative model-framework for spatio-temporal effects in...

引用

IEEE International Workshop on Cellular Nanoscale Networks and their Applications, (CNNA)

作者： D. Balya B. Roska E. Nemeth T. Roska F. Werblin Analogical & Neural Computing Laboratory Computer and Automation Research Institute Budapest Hungary Dision Research Laboratory Deparhnent of Molecular and Cell Biology University of California Berkeley Berkeley CA USA

Retinal models based on the cellular neural network (CNN) paradigm have been widely used. These neuromorphic models are based on retinal anatomy and physiology. In this paper a framework is proposed for qualitative spatio-temporal studies in vertebrate retinas, the underlying retinal anatomy is followed as closely as possible, the characteristics of the physiological models, however, are kept simple. The goal is to model the qualitative effects, since the developed models are simple, compared to a fully neuromorphic one, we have a good chance to implement them on CNN Universal Machine chips using multi-layer technology.

关键词： Retina Biological system modeling Cellular neural networks Prototypes Neuromorphics Biology computing computer networks Laboratories Biological cells Anatomy

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Motion segmentation and tracking optimization with edge relaxation in the cellular nonlinear network architecture

Motion segmentation and tracking optimization with edge rela...

引用

IEEE International Workshop on Cellular Nanoscale Networks and their Applications, (CNNA)

作者： L. Czuni T. Sziranyi Department of Image Processing and Neurocomputing University of Veszpém Veszprem Hungary Analogical and Neural Computing Laboratory Computer and Automation Institute Hungarian Academy of Sciences Budapest Hungary

We have developed a high-level set of cellular neural net (CNN) functions for finding the segment-borders of moving objects through spatio-temporal relaxation optimization. They are analogic algorithms based on simple CNN instructions considering their implementability in analogic VLSI chips. Motion information extraction from video series is very power consuming. Most computing effort is devoted to motion vector field estimation, object definition and boundary determination. Finding interrelations among small segments, obtained by oversegmentation, involves optimization through merging or separation. In our algorithm the process starts from an oversegmented image, then the segments are merged using information from spatial and temporal auxiliary data: motion fields and motion history. This grouping process is based on neighboring segment similarity in color, speed and time-depth. There is also a feedback to accept or refuse the cancellation of a segment-border. Our parallel approach is independent of the number of segments or objects. We use simple VLSI functions. We develop grouping by stochastic optimization. This relaxation-based motion segmentation can be a basic step of the effective coding of image-series and other automatic motion tracking systems. The proposed system is planned to implement in a cellular nonlinear network chip-set architecture.

关键词： Motion segmentation computer vision Tracking Image segmentation Cellular neural networks Very large scale integration neural networks Data mining Motion estimation Merging

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CNN technology in action

CNN technology in action

引用

IEEE International Workshop on Cellular Nanoscale Networks and their Applications, (CNNA)

作者： A. Zarandy S. Espejo P. Foldesy L. Kek G. Linan C. Rekeczky A. Rodriguez-Vazquez T. Roska I. Szatmari T. Sziranyi P. Szolgay Analogical and Neural Computing Laboratory of the Computer and Automation Research Institute Hungarian Academy of Sciences (ATOMKI) Hungary Institute of Microelectronics of Seville Spanish National Microelectronics Center Spain

Two Cellular neural Net Universal Machine (CNN-UM) prototypes are demonstrated in action. The first one is the latest 4096 cell-processor, analog I/O, analogic CNN visual microprocessor, on which online video image processing will be performed. The second one, the 20/spl times/22 binary input-binary output CNN-UM chip is introduced as an ultra high-speed focal plane array processor. In the live demonstration it captures and classifies 10000 frames in a second.

关键词： Cellular neural networks Microelectronics Microprocessors Image processing Prototypes Laboratories Engines Condition monitoring Cellular networks Logic

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Analogic computing: system aspects of analogic CNN sensor computers

Analogic computing: system aspects of analogic CNN sensor co...

引用

IEEE International Workshop on Cellular Nanoscale Networks and their Applications, (CNNA)

作者： T. Roska Analogical and Neural Computing Laboratory of the Computer and Automation Research Institute of the Hungarian Academy of Science Budapest and the Department of Information Technology Pfizmhy P. Catholic University Budapest Hungary

A new principle of computing and computers is emerging: the analogic cellular computer. Its architecture, the Cellular neural Net (CNN) Universal Machine, is now implemented in several different physical forms and the first practical experiments exhibit very good frame rate and computing power. Several "Kilo real-time video" frame rate (more than 10000 frames per second) and TeraOPS computing power on a 1 cm/sup 2/ CMOS (0.5 micron) chip were measured. In this review article, the systems aspects and the new directions in this field are considered. A new world of software and a new notion of computing are taking ground. The possibility of software in optical computing becomes feasible. Likewise, programming on atomic and molecular scale implementations may be possible. Hence, photons and molecules may be used for signal representation in these analogic computers.

关键词： Cellular neural networks Sensor systems Optical computing computer architecture neural networks Turing machines Physics computing Power measurement Semiconductor device measurement Atomic measurements

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Spatio-temporal segmentation with edge relaxation and optimization using fully parallel methods

Spatio-temporal segmentation with edge relaxation and optimi...

引用

International Conference on Pattern Recognition

作者： T. Sziranyi L. Czuni Analogical and Neural Computing Laboratory Computer & Aut. Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary Department of Image Processing and Neurocomputing University of Veszprém Veszprem Hungary

ISBN: (纸本)0769507506

In this paper we outline a fully parallel and locally connected computation model for the spatio-temporal segmentation of motion events in video sequences. We are searching for a new algorithm, which can be easily implemented in one-pixel/one-processor cell-array VLSI architectures at high-speed. Our proposed algorithm starts from an oversegmented image, then the segments are merged by applying the information coming from the spatial and temporal auxiliary data: motion fields and motion history, which is calculated from consecutive image frames. This grouping process is defined through a similarity measure of neighboring segments, which is based on the values of intensity, speed and the time-depth of motion history. As for checking the merging process there is a feedback implemented, by that we can accept or refuse the cancellation of a segment-border. Our parallel approach is independent of the number of segments and objects, since instead of graph representation and serial processing of these components, image features are defined on the pixel-level. We use simple functions, easily realizable in VLSI, like arithmetic and logical operators, local memory transfers and convolution.

关键词： Image segmentation Very large scale integration History Concurrent computing Computational modeling Video sequences computer architecture Motion measurement Velocity measurement Merging

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A comparison of the different CNN implementations in solving the problem of spatiotemporal dynamics in mechanical systems

A comparison of the different CNN implementations in solving...

引用

IEEE International Workshop on Cellular Nanoscale Networks and their Applications, (CNNA)

作者： P. Szolgay T. Hidvegi Z. Szolgay P. Kozma Analogical and Neural Computing Laboratory Computer and Automation Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary Department of Mechanics Technical University of Budapest Budapest Hungary Image processing and Neurocomputing University of Veszprém Veszprem Hungary

The large computing power of cellular neural networks (CNNs) is used to compute the transient response of a mechanical vibrating system. A basic question is how an inhomogeneous mechanical system can be computed by using different CNNUM (cellular neural net Universal Machine ) implementations. How complexity to time transformations were used in different CNNUM implementations, namely in software, in emulated digital CNN chips and in analog chips is discussed.

关键词： Cellular neural networks Spatiotemporal phenomena Mechanical systems computer networks Equations Damping Boundary conditions Laboratories Automation Transient response

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