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Feature extraction of postage stamps using an iterative approach of CNN

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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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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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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Solution of stereo correspondence in real scene: an analogic CNN algorithm

Solution of stereo correspondence in real scene: an analogic...

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

作者： A.G. Radvanyi Analogic & Neural Computing Systems Laboratory Computer and Automation Institute Hungarian Academy of Sciences Budapest Hungary

A multilayer analogic CNN (cellular neural network) algorithm is proposed to reconstruct surfaces from real scene stereo-pairs consisting of two images shot from one and the same camera position. The whole scene is illuminated by randomly structured light to provide texture on otherwise "empty" surfaces. One image is that of the illuminated background, the other one is the image of illuminated objects in front of the same background.< >

关键词： Layout Cellular neural networks Image reconstruction Surface reconstruction Stereo vision Surface texture Matched filters Laboratories automation Nonhomogeneous media

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CASTLE: an emulated digital CNN architecture design issues, new results

CASTLE: an emulated digital CNN architecture design issues, ...

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IEEE International Conference on Electronics, Circuits and systems (ICECS)

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

A new emulated digital CNN universal machine chip architecture is introduced in this paper. The chip contains 24 CNN core processors using 70 mm/sup 2/ silicon area with a 0.35 /spl mu/m technology. Its speed is 1 ns/virtual cells/CNN iterations with 12 bit precision. This enables one to execute over five hundred 3/spl times/3 convolutions on each frame of a 240/spl times/320 sized 25 fps (standard NTSC) digital image flow. Another new feature of the design is the different precision capability. This allows the user to convert precision to speed.

关键词： Cellular neural networks computer architecture Optical feedback Equations Turing machines Silicon Convolution Image processing Very large scale integration Biomedical optical imaging

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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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The computational infrastructure for cellular visual microprocessors

The computational infrastructure for cellular visual micropr...

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Proceedings of the International Conference on Microelectronics for neural, Fuzzy and Bio-Inspired systems, MicroNeuro

作者： P. Szolgay A. Zarandy S. Zold T. Roska P. Foldesy L. Kek T. Kozek K. Laszlo I. Petras Cs. Rekeczky I. Szatmari D. Balya Analogic and Neural Computing Systems Laboratory Computer and Automation Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

A new computational paradigm is emerging for spatio-temporal problems: analogic CNN array-computing. The elementary instructions and programming techniques, however, are drastically different from any other computers. These elementary instructions represent complex, spatio-temporal, nonlinear, dynamic phenomena including all the standard and exotic properties of simple image processing operators as well as waves, patterns, and "evolving systems". Meanwhile, a computational infrastructure has also emerged interfacing this revolutionary computing technology to digital systems as well as enabling the development of high-level software. The main goal was to provide easy-to-use tools and hardware interfacing elements, and to put most of the sophistication in the chips and chip sets.

关键词： Microprocessors Cellular neural networks Prototypes computer aided instruction Image processing Digital systems Hardware Turing machines Embedded computing CMOS technology

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An emulated digital architecture implementing the CNN Universal Machine

An emulated digital architecture implementing the CNN Univer...

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

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

An emulated digital VLSI CMOS architecture is described, where the main features are as follows: (i) variable accuracy, (ii) a complete CNN Universal Machine on the silicon, (iii) a good area time trade off. The whole architecture was defined on VHDL and the following key parameters of the implementation were computed namely, (i) the speed (1 ns/virtual cell/iteration), (ii) the number of the physical processing cells per cm/sup 2/ is 24 by using 0.35 /spl mu/m three metal layer CMOS technology.

关键词： Cellular neural networks Turing machines computer architecture CMOS technology Physics computing Digital signal processing Logic Very large scale integration CMOS process Silicon

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Efficient off-line feature selection strategies for on-line classifier systems

Efficient off-line feature selection strategies for on-line ...

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International Joint Conference on neural Networks (IJCNN)

作者： D. Balya G. Timar I. Szatmari Cs. Rekeczky AnaLogic and Neural Computing Systems Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

In this work we discuss feature/signature selection strategies for on-line classifier systems, implemented on a common stand-alone HW/SW vision system. In the chosen computational environment topographic and non-topographic computing can be combined for the targeted task of terrain feature analysis. The topographic front-end of the system is capable of deriving more than thousand different visual signatures per second from a few dozen visual feature channels. These signatures are statistically correlated and might also be computationally inter-dependent. To develop an efficient real-time classification algorithm the dimensionality of the extracted features has to be significantly reduced, i.e. only a few signatures can be selected. In this paper we analyze different signature selection strategies for various classifiers with given performance criteria and system-level time performance constraints. Two blind signature selection techniques are described: variance maximization and a factor-based analysis. We also discuss supervised selection mechanisms: class-based statistical and decision-tree-based approaches.

关键词： Feature extraction computer vision Classification algorithms Performance analysis Hardware Microprocessors Laboratories automation Machine vision Time factors

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