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International Conference on Pattern Recognition

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

We developed an augmented reality tool for vision-based hand gesture recognition in a camera-projector system. Our recognition method uses modified Fourier descriptors for the classification of static hand gestures. Hand segmentation is based on a background subtraction method, which is improved to handle background changes. Most of the recognition methods are trained and tested by the same service-person, and training phase occurs only preceding the interaction. However, there are numerous situations when several untrained users would like to use gestures for the interaction. In our new practical approach the correction of faulty detected gestures is done during the recognition itself. Our main result is the quick on-line adaptation to the gestures of a new user to achieve user-independent gesture recognition.

关键词： computer displays Cameras Fault detection Mice Hardware Phase detection System testing Feedback Auditory displays Image processing

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CNN Universal Machine as Classificaton Platform: An ART-like Clustering Algorithm

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International Journal of neural systems 2003年第6期13卷 415-425页

作者： Balya, David Analogical and Neural Computing Laboratory HAS Computer and Automation Research Institute Kende u. 13 BudapestH-1111 Hungary

Fast and robust classification of feature vectors is a crucial task in a number of real-time systems. A cellular neural/nonlinear network universal machine (CNN-UM) can be very efficient as a feature detector. The next step is to post-process the results for object recognition. This paper shows how a robust classification scheme based on adaptive resonance theory (ART) can be mapped to the CNN-UM. Moreover, this mapping is general enough to include different types of feed-forward neural networks. The designed analogic CNN algorithm is capable of classifying the extracted feature vectors keeping the advantages of the ART networks, such as robust, plastic and fault-tolerant behaviors. An analogic algorithm is presented for unsupervised classification with tunable sensitivity and automatic new class creation. The algorithm is extended for supervised classification. The presented binary feature vector classification is implemented on the existing standard CNN-UM chips for fast classification. The experimental evaluation shows promising performance after 100% accuracy on the training set. © 2003 World Scientific Publishing Company.

关键词： Classification (of information)

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Feature guided visual attention with topographic array processing and neural network-based classification

Feature guided visual attention with topographic array proce...

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International Joint Conference on neural Networks (IJCNN)

作者： G. Timar D. Balya I. Szatmari C. Rekeczky Analogical & Neural Computing Laboratory Computer and Automation Research Institute Budapest Hungary

Biological systems are constantly engulfed in sensory input that must be processed. Attention has evolved to cut down on the magnitude of the input and enable the agent to analyze the most important parts of the information. This is especially true for the visual system where the appropriate field of view and scale must be determined. Our system receives a video flow with considerably higher resolution than the resolution of the cellular neural net based visual microprocessor that computes the topographic features of the input. This process requires a dynamic positioning of the processing window in the video flow. We have developed a fast attention and selection algorithm that allows the system to choose the field of view and scale (zoom) level for the next frame based on the features computed from the current frame and the output of the ART or NNC-based classifiers. The algorithmic framework and hardware architecture of the system are presented along with experimental chip results for several video flows recorded in flying vehicles.

关键词： Array signal processing neural networks Biological systems Information analysis Visual system Cellular neural networks Microprocessors Vehicle dynamics Subspace constraints Hardware

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Detecting global spatial-temporal events: saccadic suppression

Detecting global spatial-temporal events: saccadic suppressi...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： D. Balya Analogical & Neural Computing Laboratory Computer and Automation Research Institute Budapest Hungary

You may think that while are watching a schema, your eyes move smoothly and continuously to investigate the circuitry, but that is not the case. Your eyes fixate on a part of the circuit hold for a moment then suddenly jump to a new position where a particular visual detail attracts your eyes. During this jump or saccade the eyes move rapidly and we hardly notice that, relative to our eye position, the entire visual world globally shifts. Vision is in some sense turned off during saccades, which is called saccadic suppression. This paper presents a neuromorphic algorithm to detect these spatial-temporal events from the input flow. The algorithm of the saccadic suppression event detection is implemented on the standard cellular nonlinear network universal machine environment. The processing time is roughly one millisecond on the ACE4k CNN-UM chip in the Aladdin environment, and therefore it can serve as a common function in any real-time spatial-temporal algorithm. The saccadic suppression event detector can be used in different areas such as video shot detection or to define the instant when the time-dependent part of the algorithm, e.g. homotopy, has to be initialized.

关键词： Event detection Eyes Change detection algorithms Lighting Retina Circuits Gunshot detection systems Layout Cameras Switches

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Bio-inspired flight control and visual search with CNN technology

Bio-inspired flight control and visual search with CNN techn...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： C. Rekeczky D. Balya G. Timar I. Szatmari AnaLogical and Neural Computing Systems Laboratory Computer and Automation Institute of the Hungarian Academy of Sciences Budapest Hungary Analogical and Neural Computing Systems Laboratory Computer and Automation Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

In this paper it is shown that by building on parallel topographic CNN preprocessing of image flows, efficient terrain exploration and visual navigation algorithms can be developed. The approach combines several channels of nonlinear spatio-temporal feature detectors within an analogic CNN algorithm and produces unique binary maps of salient feature locations. This preprocessing scheme is embedded into a multi-target tracking (MTT) framework where these features are statistically described and assigned to numbered tracks. The MTT output has two distinct roles. First, its feature descriptors drive a classifier based on the adaptive resonance theory (ART), which is also implemented on CNN architecture. Second, it provides an optical flow ("target displacement") estimate to the navigation system, which in turn calculates the flight control parameters (Yaw-Pitch-Roll). An upper level visual attention and selection mechanism uses both the feature descriptors and the optical flow estimates to automatically adjust the focus and scale (zoom) during navigation. The paper describes the architecture and the algorithmic frameworks and provides the first experimental results on aerial video-flows.

关键词： Aerospace control Cellular neural networks Navigation Target tracking Biomedical optical imaging Nonlinear optics Image motion analysis computer vision Detectors Resonance

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Analysis of Multidimensional neural Activity Via CNN-UM

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International Journal of neural systems 2003年第6期13卷 479-487页

作者： Gal, Viktor Grun, Sonja Tetzlaff, Ronald Analogical and Neural Computing Laboratory Hungarian Academy of Sciences Computer Automation Research Institute Ldgymdnyosi u. 13 BudapestH-1111 Hungary Dept. Neurophysiology Max Planck Inst for Brain Research Deutschordnerstr. 46 Frankfurt/MainD-60528 Germany Institute for Applied Physics University of Frankfurt Robert-Mayer-Str. 2-4 Frankfurt/MainD-60325 Germany

In this paper we show that the Cellular Nonlinear Network Universal Machine (CNN-UM) is an excellent tool for analyzing time series of multidimensional binary signals. The developed algorithm is dedicated to process electrophysiological multi-neuron recordings: our aim is to find specific multidimensional activity patterns, which may reflect higher order functional cell-assemblies. The analysis consists of two parts: first, the occurrences of different patterns are counted, then the statistical significance of each occurrence frequency is calculated separately. © 2003 World Scientific Publishing Company.

关键词： Electrophysiology

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Fast and efficient multi-layer CNN-UM emulator using FPGA

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Periodica Polytechnica Electrical Engineering 2003年第1-2期47卷 57-70页

作者： Nagy, Zoltán Szolgay, Péter Department of Image Processing University of Veszprém Egyetem u. 10 H-8200 Veszprém Hungary Analogic/Neural Computing Laboratory Computer and Automation Institute HAS Kende u. 13-17 H-1111 Budapest Hungary

A new emulated digital multi-layer CNN-UM chip architecture called Falcon has been developed. Simulation running time can be hundred times shorter using the Falcon processor array compared to the software simulation. This huge computing power makes real time image processing possible. In this paper the main steps of the FPGA implementation and optimization are introduced. The Distributed Arithmetic technique is used to optimize the architecture on FPGAs. Using this technique, smaller and faster arithmetic units can be designed than using conventional approach where multiplier cores and adder trees are used to compute the state equation of the CNN array.

关键词： Program processors

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An accelerated digital CNN-UM (CASTLE) architecture by using the pipe-line technique

An accelerated digital CNN-UM (CASTLE) architecture by using...

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

作者： T. Hidvegi P. Keresztes P. Solgay Analogic and Neural Computing Systems Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary Analogic and Neural Computing Systems Laboratory Hungarian Academy of Sciences Budapest Hungary István Széchenyi University Gyôr Hungary

Different CNN-UM architecture implementations, analog and emulated digital, were developed. The emulated digital architecture (CASTLE) is accurate but slower than the analog CNN-UMs. It is generally disadvantageous especially if transient computing is critical. The operation speed of the emulated digital implementations, namely CASTLE, can be increased significantly using the pipeline technique. This solution is analyzed with respect to area, time, etc. These arithmetic cores were tested and simulated using a VIRTEX FPGA development system.

关键词： Acceleration computer architecture Cellular neural networks Arithmetic Registers automation Image processing System testing Computational modeling Field programmable gate arrays

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CNN based color constancy algorithm

CNN based color constancy algorithm

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

作者： L. Torok A. Zarandy Computer and Automation Reseaxh Institute Analogic and Neural Computing Laboratory Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

ISBN: (纸本)981238121X

Color constancy (CC) is a perceptional phenomena in which living species, capable of color vision, perceive object color apart from the spectral distribution of light applied to illuminate the objects. The algorithm that can recover objects' original color and display them as if they were illuminated by spectrally even (white) light is called the CC algorithm. In contrast to other solutions our approach offers on-line possibilities in applications as its operation needs consist of mainly local interactions that are well suited to the architecture of cellular neural/non-linear networks (CNN). In a recent paper, we offered a brief survey of common CC approaches, introduced the principles of our CC algorithm, compared ACE4K on-chip results versus simulation, examined the robustness of our algorithm and outlined a newly developed setup for reliable color image recording.

关键词： Cellular neural networks Lighting Robustness Color Semiconductor device modeling Humans computer vision Distributed computing Cellular networks Layout

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A realistic mammalian retinal model implemented on complex cell CNN universal machine

A realistic mammalian retinal model implemented on complex c...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： D. Balya C. Rekeczky T. Roska Analogical & Neural Computing Laboratory Computer and Automation Research Institute Budapest Hungary

The visual system is probably the most important sensory modality for humans as well as for mammals. Its first and best-known part is the retina, which is not a mere photoreceptor or static camera but a sophisticated feature preprocessor with a continuous input and several parallel output channels. These channels build up a "visual language" and any realistic mammalian retina model should generate the elements of this visual language. The framework of mammalian retinal modeling via multi-layer CNN has been recently published. In the present paper we show the transformation of this model into a CNN-UM algorithm and the design steps of the implementation of this complex visual language. The analogic algorithm consists of a series of different complex-cell CNN dynamics. The algorithm is feasible on a recently fabricated complex cell CNN-UM chip. The decomposition method of the multilayer mammalian retina model will be discussed in detail.

关键词： Retina Cellular neural networks Turing machines Visual system Humans Photoreceptors Cameras Data preprocessing Algorithm design and analysis Heuristic algorithms

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