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FORMING AN INFORMATION TECHNOLOGY DEPARTMENT IN TURBULENT TIMES

IEEE COMMUNICATIONS MAGAZINE 1992年第11期30卷 52-56页

作者： ROSKA, T Dual and Neural Computing Systems Research Laboratory Computer and Automation Institute Hungarian Academy of Sciences (ATOMKI) Hungary

The goals, objectives, characteristics, prerequisites, and courses of an information technology curriculum developed at the University of Veszprem in Hungary are discussed. Basic, introductory, and core courses as well as special subjects are listed, and the program schedule of the five years is presented. The place and role of information engineering in a changing world is discussed, including some thoughts on the role of information technology in the general university curricula.< >

关键词： Information technology Laboratories Education Educational technology Automation Europe Mechanical engineering Electrical engineering computing Informatics Solids

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HIGH-SPEED CHARACTER-RECOGNITION USING A dual CELLULAR neural-NETWORK ARCHITECTURE (CNND)

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IEEE TRANSACTIONS ON CIRCUITS AND systems II-EXPRESS BRIEFS 1993年第3期40卷 223-231页

作者： SZIRANYI, T CSICSVARI, J Analogic and Neural Computing Systems Research Laboratory Computer and Automation Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

An effective new character recognition procedure implemented on a new type of hardware system is proposed. This procedure applied a new architecture, called CNND. This CNND contains one or more analog cellular neural Networks (CNN) and some digital logic, incorporating the advantages of the fast analog CNN signal processing and the fast and easy decision capability of digital logics. This paper shows that this CNND system can be used for recognition of multifont printed or handwritten characters. Implemented in hardware, the system could hit the 100 000 char/s recognition speed with a recognition rate of more than 95 %. We show that the CNN results of pictures (maximum 40 * 40 pixels) of printed characters can be coded into about n * 20 bits (n = 2 ... 6) , so the coded results can be used to address memories of about 1 MB. The codes of CNN results of possible character pictures are used to address the memories while the memory contents are filled by the character categories. Prior to the hardware implementation the decision memories are filled by the results of recognition simulation for the possible pictures of each character-class in a filling procedure. In the memory filling procedure the simulated recognition uses a new random-type nearest neighbor (NN) method, which is ideal for the recent proposal of hardware applications. Recognition of handwritten characters is demonstrated in the same system with good recognition accuracy.

关键词： Character recognition Cellular neural networks Image edge detection Handwriting recognition neural networks Detectors Logic Hardware Charge coupled devices Filling

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A two-layer Radon transform cellular neural network

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IEEE Transactions on Circuits and systems II: Analog and Digital Signal Processing 1992年第7期39卷 488-489页

作者： Wu, Chai Wah Chua, Leon O. Roska, Tamas Department of Electrical Engineering and Computer Sciences University of California Berkeley CA USA Dual and Neural Computing Systems Research Laboratory Computer and Automation Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

A two-layer continuous-time cellular neural network for finding the Radon transform of a binary image is presented. The functionality of this cellular neural network follows from the functionality of the connected com... 详细信息

关键词： Image processing

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Using cellular neural network to "see" random-dot stereograms 5th

Using cellular neural network to "see" random-dot stereogram...

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5th International Conference on Computer Analysis of Images and Patterns, CAIP 1993

作者： Radványi, András G. Dual and Neural Computing Systems Laboratory Computer & Automation Institute Hungarian Academy of Sciences Bp POB 63 H-1518 Hungary

ISBN: (纸本)9783540572336

The Cellular neural Networks (CNN) providing for efficient analog array processing of images are used for revealing surface features hidden in different types of random-dot stereograms (RDS) coding 3D information in internal correlation. Although originally random-dot stereograms are for probing human stereopsis, they may also find application in engineering practice based on the method proposed for creating stereograms of real objects in optical environment using projector and camera. Also the concept of difference surface and difference stereogram is introduced and used for coding smooth surfaces. © 1993, Springer Verlag. All rights reserved.

关键词： Cellular neural networks

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Robustness of cellular neural networks in image deblurring and texture segmentation

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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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SUBPIXEL PATTERN-RECOGNITION BY IMAGE HISTOGRAMS

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PATTERN RECOGNITION 1994年第8期27卷 1079-1092页

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

Recognition of small patterns covering only a few pixels in an image cannot be done by conventional recognition methods. A theoretically new pattern recognition method has been developed for undersampled objects which are (much) smaller than the window-size of a picture element (pixel), i.e. these objects are of subpixel size. The proposed statistical technique compares the gray-level histogram of the patterns of a set of scanned objects to be examined with the (calculated) gray-level densities of different (in shape or size) possible objects, and the recognition is based on this comparison. This method does not need high-precision movement of scanning sensors or any additional hardware. Moreover, the examined patterns should be randomly distributed on the screen, or a random movement of camera is (or target or both are) needed. Effects of noise are analysed, and filtering processes are suggested in the histogram domain. Several examples of different object shapes (triangle, rectangle, square, circle, curving lines, etc.) are presented through simulations and experiments. A number of possible application areas are suggested, including astronomy, line-drawing analysis and industrial laser measurements.

关键词： SUBPIXEL-RECOGNITION HISTOGRAM NOISE FILTERING UNDERSAMPLING SUPERRESOLUTION IMAGE ANALYSIS PATTERN CLASSIFICATION CONVOLUTION STATISTICAL PATTERN RECOGNITION LIGHT-SENSOR ARRAYS DENSITY ESTIMATION

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Statistical subpixel pattern recognition by histograms

Statistical subpixel pattern recognition by histograms

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International Conference on Pattern Recognition

作者： T. Sziranyi Dual and Neural Computing Systems Research Laboratory Computer and Automation Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

A new statistical pattern recognition method has been developed for detection, recognition or measurement of patterns which are (much) smaller than the measure of the elementary pixel windows in the image screen. In this measurement the gray-level histogram of the objects examined is compared with the simulated histograms of different (in type or size) possible objects, and the recognition (of shape or measure) is taken on the basis of the comparison. This method does not need ultra-precise movement of the scanning sensors or any additional hardwares. Moreover, the examined pattern should be randomly distributed on the screen, or a random movement of camera (or target or both) is needed. Effect of noises are analyzed, and filtering processes are suggested in the histogram domain. Several examples of different shapes are presented through simulations and experiments.< >

关键词： Pattern recognition Histograms Shape measurement Size measurement Image recognition Pixel Hardware Cameras Noise shaping Filtering

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Markov random field image segmentation using cellular neural network

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IEEE TRANSACTIONS ON CIRCUITS AND systems I-FUNDAMENTAL THEORY AND APPLICATIONS 1997年第1期44卷 86-89页

作者： Sziranyi, T Zerubia, J Analogical and Neural Computing Systems Research Laboratory Computer and Automation Institute Hungarian Academy of Sciences (MTA-SZTAKI) Budapest Hungary I.N.R.I.A. Sophia Antipolis Sophia-Antipolis France

Markovian approaches to early vision processes need a huge amount of computing power. These algorithms can usually be implemented on parallel computing structures. with the Cellular neural Networks (CNN), a new image processing toot is coming into consideration, Its VLSI implementation takes place on a single analog chip containing several thousands of cells. Herein se use the CNN UM architecture for statistical image segmentation. The Modified Metropolis Dynamics (MMD) method can be implemented into the raw analog architecture of the CNN, We are able to implement a (pseudo) random held generator using one layer (one memory/cell) of the CNN. We can introduce the whole pseudostochastic segmentation process in the CNN architecture using 8 memories/cell, We use simple arithmetic functions (addition, multiplication), equality-test between neighboring pixels and very simple nonlinear output functions (step, jigsaw, With this architecture, a real VLSI CNN chip can execute a pseudostochastic relaxation algorithm of about 100 iterations in about 1 ms, In the proposed solution the segmentation is unsupervised. We have developed a pixel-level statistical estimation model. The CNN turns the original image into a smooth one. Then we have two gray-level values for every pixel: the original and the smoothed one. These two values are used for estimating the probability distribution of region label at a given pixel. Using the conventional first-order Markov Random Field (MRF) model, some misclassification errors remained at the region boundaries, because of the estimation difficulties in case of low SNR. By using a greater neighborhood, this problem has been avoided. In our CNN experiments, we used a simulation system with a fixed-point integer precision of 16 bits, Our results show that even in the case of the very constrained conditions of value-representations (the interval is (-64, +64), the accuracy is 0.002) can result in an effective and acceptable segmentation.

关键词： Markov processes VLSI analogue integrated circuits analogue processing circuits cellular neural nets estimation theory image segmentation neural chips neural net architecture parallel algorithms probability CNN UM architecture Markov random field image s Parallel algorithms Cellular neural networks neural network hardware Probability estimation theory image segmentation neural net architecture Analog integrated circuits analogue processing circuits Very large scale integration Markov random fields Markov chain

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The CNN Universal Machine part 2: Programmability and applications 2

The CNN Universal Machine part 2: Programmability and applic...

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2nd International Workshop on Cellular neural Networks and their Applications, CNNA 1992

作者： Roska, Tamás Chua, Leon O. Electronics Research Laboratory Department of Electrical Engineering and Computer Science University of California at Berkeley BerkeleyCA94720 United States Dual and Neural Computing Systems Laboratory Computer and Automation Institute of the Hungarian Academy of Sciences Kende-u.13/17 BudapestH-1111 Hungary

ISBN: (纸本)0780308751

The programmability (as a stored program) of the CNN Universal Machine is discussed first. It is shown why and in which sense this machine is universal. A new type of algorithm, the analogic one, is introduced. The application potential is reviewed and the biological relevance is analyzed. It turns out that the architecture is optimal not only for silicon implementations, however, many biological information processing organs have the same structure. © 1992 IEEE.

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The CNN Universal Machine part 1: The architecture 2

The CNN Universal Machine part 1: The architecture

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2nd International Workshop on Cellular neural Networks and their Applications, CNNA 1992

作者： Chua, Leon O. Roska, Tamás Electronics Research Laboratory Department of Electrical Engineering and Computer Science University of California at Berkeley BerkeleyCA94720 United States Dual and Neural Computing Systems Laboratory in the Computer and Automation Institute of the Hungarian Academy of Sciences Kende-u.13/17 BudapestH-1111 Hungary

ISBN: (纸本)0780308751

Various types of CNNs are summarized and the taxonomy of CNN is given according to the different types of grids, processors, interactions, and modes of operation. Next, the CNN Universal Machine is introduced. The architecture and the key features are outlined. A quite exhaustive list of references completes the paper. © 1992 IEEE.

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