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Robustness of cellular neural networks in image deblurring and texture segmentation

INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS 1996年第3期24卷 381-396页

作者： Sziranyi, T Analogical and Neural Computing Systems Research Laboratory Computer and Automation Institute Hungarian Academy of Sciences (MTA SZTAKI) pf. 63 H-1518 Budapest Hungary

Template parameters of cellular neural networks (CNNs) should be robust enough to random variability of VLSI tolerances and noise. Using the CNN for image processing, one of the main problems is the robustness of a given task in a real VLSI chip. It will be shown that very different tasks such as 2D or 3D deconvolution and texture segmentation can be solved in a real VLSI CNN environment without significant loss of efficiency and accuracy under low precision (about 6-8 bits) and random variability of the VLSI parameters. The CNN turns out to be very robust against template noise, image noise, imperfect estimation of templates and parameter accuracy. The parameters of a template are tuned using genetic learning. These optimized parameters depend on the precision of the architecture. It was found that about 6-8 bits of precision is enough for a complicated multilayer deconvolution, while only 4 bits of precision is enough for difficult texture segmentation in the presence of noise and parameter variances, The tolerance sensitivity of template parameters is considered for VLSI implementation. Theory and examples are demonstrated by many results using real-life microscopic images and natural textures.

关键词： Cellular neural networks

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Spatial depth extraction using random stereograms in analogic CNN framework

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INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS 1996年第1期24卷 69-92页

作者： Radvanyi, AG Analogical and Neural Computing Systems Laboratory Computer and Automation Institute Hungarian Academy of Sciences POB 63 H-1518 Budapest Hungary

Demonstrated and motivated on human stereo vision analogic CNN algorithms are proposed to extract 3D spatial information from computer-generated random-dot stereograms as well as real scene random-dot like ones produced with simple optical devices, projector and camera. Several aspects of making real scene stereograms are considered to minimize perspective distortion and enable local CNN processing.

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Feature extraction of postage stamps using an iterative approach of CNN

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND computer SCIENCES 1996年第10期E79A卷 1741-1746页

作者： Kishida, J Rekeczky, C Nishio, Y Ushida, A Faculty of Engineering Tokushima University Tokushima-shi 770 Japan Analogical and Neural Computing Systems Laboratory Computer and Automation Institute Hungarian Academy of Sciences 27 H-1111 Budapest Kende-u. 13-17 Hungary

In this article, a new analogic CNN algorithm to extract features of postage stamps in fray-scale images is introduced. The Gradient Controlled Diffusion method plays an important role in the approach. In our algorithm, it is used for smoothing and separating Arabic figures drawn with a color which is similar to the background color. We extract Arabic figures in postage stamps by combining Gradient Controlled Diffusion with nearest neighbor linear CNN template and logic operations. Applying the feature extraction algorithm to different test images it has been verified that it is also effective in complete segmentation problems.

关键词： cellular neural networks analogic algorithm image processing feature extraction gradient controlled diffusion

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Distance preserving 1D Turing-wave models via CNN, implementation of complex-valued CNN and solving a simple inverse pattern problem (detection)

Distance preserving 1D Turing-wave models via CNN, implement...

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

作者： G. Toth P. Foldesy T. Roska Analogic and Neural Computing Systems Laboratory Computer and Automation Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

In this paper a cellular neural network (CNN) implementation of a reaction-diffusion system is described, which produces distance preserving periodic Turing patterns. The CNN with complex-valued templates are introduced, presenting an application for pattern generation. Finally a method for black-and-white pattern detection is described.

关键词： Cellular neural networks Equations Skin Marine animals Laboratories automation Physics Chemistry Biological system modeling Inhibitors

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Adaptive histogram equalization with cellular neural networks

Adaptive histogram equalization with cellular neural network...

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

作者： M. Csapodi T. Roska Analogic and Neural Computing Systems Laboratory Computer and Automation Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

Adaptive histogram equalization (AHE), a method of contrast enhancement which is sensitive to local spatial information in image, has demonstrated its effectiveness in many applications. However, this technique is computationally intensive. In this paper we present two computational methods designed to fit well onto the locally interconnected array computer architecture of cellular neural networks (CNNs). CNNs are well known for their image processing capabilities, specially for grey-scale medical images and images of a natural scene. In many applications it would be very useful if the operation of a template or a complex analogic algorithm were highly illumination independent. Our results suggest that we can achieve this goal by using the AHE method in a pre-processing step.

关键词： Histograms Adaptive equalizers Cellular neural networks Application software Adaptive systems computer networks Design methodology computer architecture Image processing Biomedical imaging

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Multi-scale image analysis on the CNN Universal Machine

Multi-scale image analysis on the CNN Universal Machine

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

作者： T. Kozek K.R. Crounse T. Roska L.O. Chua Analogic and Neural Computing Systems Laboratory Computer and Automation Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary Nonlinear Electronics Laboratory University of California Berkeley CA USA

Algorithms for generating multi-scale representations of gray-scale images are presented. A number of possible approaches are described to produce low-pass and band-pass decompositions using simple analogic algorithms. It is also shown how the wavelet transform can be approximated with a similar technique and be used to obtain multi-level descriptions of the input data. This paper presents some methods how the cellular neural network (CNN) Universal Machine can be used effectively for generating multi-scale representations of gray-scale imagery.

关键词： Image analysis Cellular neural networks Turing machines Convolution Laplace equations Image processing Kernel Data mining Laboratories Wavelet transforms

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Real-life application case studies using CMOS 0.8 /spl mu/m CNN universal chip: analogic algorithm for motion detection and texture segmentation

Real-life application case studies using CMOS 0.8 /spl mu/m ...

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

作者： P. Foldesy A. Zarandy P. Szolgay T. Sziranyi Analogical and Neural Computing Systems Laboratory Computer and Automation Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

Using a 20/spl times/22 CNN Universal Machine chip two application case studies are presented. A new analogic CNN algorithm is shown to detect objects having larger size than a given value on black-and-white image sequences moving in a given range of direction and speed (17 /spl mu/s processing speed could be achieved). An extremely fast texture classification analogic algorithm is given next with approximately 2 /spl mu/s processing speed and with less than 5% misclassification error rate for 4 natural textures in a real-life testing environment.

关键词： computer aided software engineering Cellular neural networks Switches Logic Motion detection Turing machines Testing Prototypes Registers Thyristors

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Optical detection of breaks and short circuits on the layouts of printed circuit boards using CNN

Optical detection of breaks and short circuits on the layout...

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

作者： P. Szolgay K. Tomordi Analogic and Neural Computing Systems Laboratory Computer and Automation Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary Image Processing and Neurocomputing Department Faculty of Engineering University of Veszprem Veszprem Hungary

The printed circuit board layout inspection methods are mostly based on local geometric information, therefore it is well suited to the cellular neural network (CNN) paradigm. Two layout errors are detected here namely, the breaks in the wires and some kind of short circuits. The designed analogic algorithms to solve the problems above were tested on real life examples using an experimental system based on our CNN-HAC1M digital multiprocessor add-on-board, with 1 million cell space and 2.0 /spl mu/s/cell/iteration speed.

关键词： Optical detectors Cellular neural networks Geometrical optics Optical computing Printed circuits Inspection Wires Algorithm design and analysis Circuit testing Life testing

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CNN chip set architectures and the visual mouse

CNN chip set architectures and the visual mouse

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

作者： T. Roska Analogical and Neural Computing Systems Laboratory Computer and Automation Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary Electronics Research Laboratory University of California Berkeley Berkeley CA USA

The cellular neural network (CNN) Universal Machine architecture when implemented in VLSI chips needs special interfaces to provide for efficient system performance both in time (speed) and space (image size). The CNN chip set architectures described solve this problem, including interfacing the possibly analog input sensors and digital output and control. Various forms of CNN Engines are presented embedding CNN chip sets. A new device, the Visual Mouse, a hand held visual supercomputer, is also presented which exploits the genuine features of CNN Engines.

关键词： Cellular neural networks Mice computer architecture Microprocessors Engines computer interfaces Spatiotemporal phenomena Buffer storage Laboratories Turing machines

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ACE: a digital floating point CNN emulator engine

ACE: a digital floating point CNN emulator engine

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

作者： B. Feher P. Szolgay T. Roska A.G. Radvanyi T. Sziranyi M. Csapodi K. Laszlo L. Nemes I. Szatmari G. Toth P.L. Venetianer Measurement and Instrument Engineering Department Technical University of Budapest Budapest Hungary Analogical and Neural Computing Systems Laboratory Computer and Automation Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

The architecture of ACE, a multiprocessor analogic cellular neural network (CNN) emulator engine consisting of 2 to 16 TMS320C40 floating point DSPs is introduced. The engine containing up to 512 Mbyte RAM (enough to store a 512/spl times/512/spl times/512 sized CNN cube) which can be controlled through its SCSI port. It can either accelerate the multilayer CNN simulator CNNM or be accessed directly from the high level, C-based analogic CNN language ACL to achieve the simulation speed of /spl sim/2.8 /spl mu/sec/cell/iteration/DSP for 3/spl times/3 linear templates.

关键词： Cellular neural networks Engines computer architecture Digital signal processing Very large scale integration Programmable logic arrays Computational modeling Logic arrays Signal processing algorithms Hardware

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