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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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CNN chip set architectures and the visual mouse

CNN chip set architectures and the visual mouse

引用

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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Supervised training based hand gesture recognition system

Supervised training based hand gesture recognition system

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作者： Licsár, Attila Szirányi, Tamás University of Veszprém Department of Image Processing and Neurocomputing Egyetem u. 10 H-8200 Veszprém Hungary Analogical and Neural Computing Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences Kende u. 13-17 H-1111 Budapest Hungary

We have developed a hand gesture recognition system, based on the shape analysis of static gestures, for Human computer Interaction purposes. Our appearance-based recognition uses modified Fourier descriptors for the classification, of hand shapes. As always found in literature, such recognition systems consist of two phases: training and recognition. In our new practical approach, following the chosen appearance-based model, training and recognition is done in an interactive supervised way: the adaptation for untrained gestures is also solved by hand signals. Our experimental results with three different users are reported. In this paper, besides describing the recognition itself, we demonstrate our interactive training method in a practical application. © 2002 IEEE.

关键词： Gesture recognition

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Picture segmentation with introducing an anisotropic preliminary step to an MRF model with cellular neural networks

Picture segmentation with introducing an anisotropic prelimi...

引用

International Conference on Pattern Recognition

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

Due to the large computation power needed for Markovian random field (MRF) based image processing, new variations of the basic MRF model are implemented. The transportation of the model to the very fast cellular neural networks (CNN) gave new tasks and opportunities to improve the technique, since the CNN has a special local architecture. This CNN architecture can be implemented in real VLSI circuits of superior speed in image processing. A type of MRF image segmentation with modified metropolis dynamics (MMD) can be well implemented in the CNN architecture. In this paper we address the improvement of this existing CNN method by introducing anisotropic diffusion as the smoothing process in the model. We suggest that this new feature with the MRF representation will give a new approach to solving early vision problems in the future.

关键词： Anisotropic magnetoresistance Cellular neural networks Image segmentation Image processing computer networks Pixel Labeling Smoothing methods Laboratories Automation

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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...

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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

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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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Efficient hardware-oriented cellular active contours

Efficient hardware-oriented cellular active contours

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： D.L. Vilarino C. Rekeczky Department Electronics and Computer Science University of Santiago de Compostela Santiago de Compostela Spain Analogical and Neural Computing Laboratory Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

In this paper, an improved algorithm for the cellular active contour technique called pixel-level snakes is proposed. The motivation is twofold: on the one hand a higher efficiency and flexibility in the contour evolution towards the boundaries of interest is pursued. On the other hand a higher performance and suitability for its hardware implementation onto a CNN chip-set architecture is required. To illustrate the validity of the proposal some examples and data about the computation time from the implementation of the algorithm on the 64/spl times/64 CNNUM chip have been included.

关键词： Active contours Pixel Data mining Automation Hardware Proposals Shape control Topology computer science Laboratories

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Analogic computing: system aspects of analogic CNN sensor computers

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

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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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On the detection of critical frequencies of a tool machine

On the detection of critical frequencies of a tool machine

引用

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

作者： P. Szolgay T. Szabo Analogical and Neural Computing Systems Laboratory Computer and Automation Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary Department of Mechanics University of Miskolc Egyetemváros Miskolc Hungary

An approximate solution is given for determining the ranges of allowed frequency of a mechanical model of a tool machine. The proposed method is based on the simulation of a measurement process of a real structure. The large computing power of cellular neural networks is used here to compute the transient response of a mechanical vibrating system.

关键词： Frequency Cellular neural networks computer networks Eigenvalues and eigenfunctions Computational modeling Finite element methods Mechanical systems Differential equations Laboratories Automation

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

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

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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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