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Tillarom: An AJAX based folk song search and retrieval system with gesture interface based on kodály hand

Tillarom: An AJAX based folk song search and retrieval syste...

1st ACM International Workshop on Human-Centered Multimedia, HCM 2006, co-located with the 2006 ACM International Multimedia Conference

作者： Licsár, Attila Szirányi, Tamás Kovács, László Pataki, Balázs University of Pannonia Department of Image Processing and Neurocomputing Egyetem u. 10 H-8200 Veszprém Hungary Department of Distributed Systems Computer and Automation Research Institute Hungarian Academy of Sciences Lágymányosi u. 11 H-1111 Budapest Hungary Analogical and Neural Computing Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences Lágymányosi u. 11 H-1111 Budapest Hungary

ISBN: (纸本)1595935002

A digital folk song search and retrieval system with a hand gesture based interface is presented. Tillarom is a comprehensive collection of original Hungarian folk songs recorded using different technologies such as phonographs and/or stereo DAT cassettes. This digital archive contains professional quality metadata records as well as MIDI recordings for presenting the different types of clustered folk songs. An AJAX based search and retrieval interface was developed that can be used together with optically recognized Kodály's hand signs to formulate queries through a web browser. The appearance based recognition of hand gestures utilizes contour analysis and SVM based classification. We evaluated the performance of the recognition of hand signs and investigated the main problems of their usage in our system. Copyright 2006 ACM.

关键词： Information systems

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CNNUM-Based Methods Using Deformable Contours on Smooth Boundaries

CNNUM-Based Methods Using Deformable Contours on Smooth Boun...

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

作者： Tamas Szabo Peter Szolgay Department of Image Processing and Neurocomputing Faculty of Information Technology University of Pannonia Veszprem Hungary Analogical and Neural Computing Systems Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

In this paper two methods are presented. A CNNUM-based method is shown to quantify the displacement of the normal interhemisperic bilateral symmetry line. The method uses a deformable open contouring technique. Anothe... 详细信息

关键词： Active contours Computed tomography Magnetic resonance Anatomical structure Image segmentation Biomedical imaging Electronic mail computer networks Pixel Image resolution

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A CNN Implementation of the Horn & Schunck Motion Estimation Method

A CNN Implementation of the Horn & Schunck Motion Estimation...

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

作者： A. Gacsadi C. Grava V. Tiponut P. Szolgay Electronics Department University of Oradea Oradea Romania Applied Electronics Department Polytechnic University of Timisoara Timisoara Romania Analogical and Neural Computing Laboratory Computer and Automation Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary Image Processing and Neurocomputing Department Veszprem University Veszprem Hungary

In this paper the parallel implementation of the Horn and Schunck motion estimation method in image sequences is presented, by using cellular neural networks (CNN). One of the drawbacks of the classical motion estimation algorithms is the computational time. The goal of the CNN implementation of the Horn & Schunck method is to increase the efficiency of the well-known classical implementation of this method, which is one of the most used algorithms among the motion estimation techniques. The aim is to obtain a smaller computation time and to include such an algorithm in motion compensation algorithms implemented using CNN, in order to obtain homogeneous algorithms for real-time applications in artificial vision or medical imaging

关键词： Cellular neural networks Motion estimation Brightness Image motion analysis Biomedical optical imaging Equations Image sequences Image processing Electronic mail Biomedical imaging

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Bi-i: A standalone ultra high speed cellular vision system

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IEEE Circuits and Systems Magazine 2005年第2期5卷 36-45页

作者： Zarándy, Ákos Rekeczky, Csaba Analogical and Neural Computing Laboratory Computer and Automation Institute Hungarian Academy of Sciences Budapest Hungary Faculty of Information Technology Péter Pázmány Catholic University Budapest Hungary Analogic Computers Ltd. Budapest Hungary

The Bi-i standalone cellular vision system is introduced and discussed. In the first part, the underlying sensor and system level architectures are presented and various implementations are overviewed. This computing platform consists of state-of-the-art sensing, cellular-sensing-processing, digital signal processing and communication devices that make it possible to use the system as an ideal computing platform for combined topographic and non-topographic calculations in sensing-processing-actuation scenarios. In the second part of the paper, ultra-high frame rate laboratory.experiments are shown and discussed to highlight the most peculiar features of the system and its applicability in various industrial quality control areas. The overview underlines the potentials of the Bi-i vision system for unmanned intelligent vehicle applications in visual exploration, identification, tracking and navigation. ©2005 IEEE.

关键词： Sensors

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High-speed label inspection system for textile industry

High-speed label inspection system for textile industry

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10th IMEKO TC10 International Conference on Technical Diagnostics

作者： Szatmári, István Zarándy, Ákos Analogical and Neural Computing Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences 13-17 Kende Street Budapest H-1111 Hungary AnaLogic Computers Ltd. 13-17 Kende Street Budapest H-1111 Hungary

ISBN: (纸本)9789638658647

In this paper, the current development of a high-speed, visual inspection unit for use in the quality assurance of narrow fabric and label weaving will be presented. The aim of the project is to provide full visual defect recognition for the textile industry. Dirt spots and patches, weaving faults, missing or torn fibers, and certain errors of the label patterns are detected. A special experimental setup was prepared for evaluating the algorithm performance. Current phase achieves a very high quality level with low false alarm rate at inspection speed of 4 m/s.

关键词： Textile industry

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Image inpainting methods by using CELLULAR neural networks

Image inpainting methods by using CELLULAR neural networks

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9th IEEE International Workshop on Cellular neural Networks and their Applications, CNNA

作者： Gacsádi, Alexandra Szolgay, Péter Electronics Department University of Oradea Str. Armatei Române Nr.5 410087 Oradea Romania Analogical and Neural Computing Laboratory Computer and Automation Institute Hungarian Academy of Sciences P.O.B.63 H-1502 Budapest Hungary Image Processing and Neurocomputing Department Veszprém University Egyetem u. 10 Veszprém Hungary

Some CNN methods are presented that can be used for the reconstruction of damaged or partially known images. The proposed methods take the possibility of direct implementation on an existing CNN chip into account, in a single step, by using 3*3 dimensional linear reaction templates. Due to complete parallel processing, computational time reduction is achieved. Efficiency of these methods can be increased by combining them with nonlinear template that ensures the growth of the local properties spreading area along with regional ones.

关键词： Cellular neural networks

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Image inpainting methods by using cellular neural networks

Image inpainting methods by using cellular neural networks

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

作者： A. Gacsadi P. Szolgay Electronics Department University of Oradea Oradea Romania Analogical and Neural Computing Laboratory Computer and Automation Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary Image Processing and Neurocomputing Department Veszprem University Veszprem Hungary

关键词： Cellular neural networks Image processing Image restoration Partial differential equations Image reconstruction Parallel processing Concurrent computing Cost function Gray-scale Laboratories

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Hand gesture recognition in camera-projector system

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8th European Conference on computer Vision, ECCV 2004 and International Workshop on Human-computer Interaction, HCI 2004

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

ISBN: (纸本)3540220127

Our paper proposes a vision-based hand gesture recognition system. It is implemented in a camera-projector system to achieve an augmented reality tool. In this configuration the main problem is that the hand surface reflects the projected background, thus we apply a robust hand segmentation method. Hand localizing is based on a background subtraction method, which adapts to the changes of the projected background. Hand poses are described by a method based on modified Fourier descriptors, which involves distance metric for the nearest neighbor classification. The proposed classification method is compared to other feature extraction methods. We also conducted tests on several users. Finally, the recognition efficiency is improved by the recognition probabilities of the consecutive detected gestures by maximum likelihood approach. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Augmented reality

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Dynamic training of hand gesture recognition system

Dynamic training of hand gesture recognition system

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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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