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

作者： Sandor Kocsardi Zoltan Nagy Arpad Csik Peter Szolgay Department of Image Processing and Neurocomputing University of Pannonia Veszprem Hungary Cellular Sensory and Wave Computing Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary Department of Mathematics and Computational Sciences Sźechenyi István University Gyor Hungary Faculty of Information Technology Pázmány Péter Catholic University Budapest Hungary

In the area of mechanical, aerospace, chemical and civil engineering the solution of partial differential equations (PDEs) has been one of the most important problems of mathematics for a long time. In this field, one of the most exciting areas is the simulation of fluid flow, which involves for example problems of air, sea and land vehicle motion. In this paper a CNN-UM based solver of 2D inviscid, adiabatic, compressible fluids will be presented. The governing equations are solved by using first- and second-order numerical methods. Unfortunately the necessity the coupled multi-layered computational structure with non-linear, space-variant templates does not make it possible to utilize the huge computing power of the analog CNN-UM chips. To improve the performance of our solution emulated digital CNN-UM implemented on FPGA has been used. Properties of the implemented specialized architecture is examined in terms of area, speed and accuracy.

关键词： Mathematical model Equations computer architecture Microprocessors Field programmable gate arrays Computational modeling Magnetic cores

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Experimental Result on Supersonic Flow Simulation on Emulated Digital CNN-UM

Experimental Result on Supersonic Flow Simulation on Emulate...

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

作者： Sandor Kocsardi Zoltan Nagy Arpad Csik Peter Szolgay Department of Image Processing and Neurocomputing University of Pannonia Veszprem Hungary Cellular Sensory and Wave Computing Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary Department of Mathematics and Computational Sciences Széchenyi István University Gyor Hungary Faculty of Information Technology Pázmány Péter Catholic University Budapest Hungary

关键词： Mathematical model Fluids computer architecture Computational modeling Equations Arrays Atmospheric modeling

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Adaptive background model

Adaptive background model

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MIPPR 2007: Automatic Target Recognition and image Analysis;and Multispectral image Acquisition

作者： Xiaochun, Lu Yijun, Xiao Zhi, Chai Bangping, Wang Graphics and Image Research Laboratory School of Computer Science Sichuan University P.O. Box 610064 Chengdu China

ISBN: (纸本)9780819469502

An adaptive background model aiming at outdoor vehicle detection is presented in this paper. This model is an improved model of PICA (pixel intensity classification algorithm), it classifies pixels into K-distributions by color similarity, and then a hypothesis that the background pixel color appears in image sequence with a high frequency is used to evaluate all the distributions to determine which presents the current background color. As experiments show, the model presented in this paper is a robust, adaptive and flexible model, which can deal with situations like camera motions, lighting changes and so on.

关键词： Target tracking

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Editorial: Knowledge engineering, semantics, and signal processing in audio - visual information retrieval

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IEEE Transactions on Circuits and Systems for Video Technology 2007年第3期17卷 257-258页

作者： Izquierdo, Ebroul Zhang, Jian Sikora, Thomas Huang, Thomas S. Department of Electrical Engineering Queen Mary University of London London El 4NS United Kingdom Sydney Australia School of Computer Science and Engineering University of New South Wales Australia Communication Systems Group Technical University Berlin Berlin Germany ITG University of Illinois at Urbana-Champaign United States Department of Electrical and Computer Engineering Coordinated Science Laboratory United States Image Formation and Processing Group Beckman Institute for Advanced Science and Technology United States Institute's Major Research Theme Human Computer Intelligent Interaction National Academy of Engineering Chinese Academies of Engineering and Sciences China International Association of Pattern Recognition Optical Society of American United States

No abstract available

关键词： Special issues and sections Knowledge engineering Information retrieval Audio-visual systems

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Ordination-fuzzy min-max neural network classifier on unlabelled pattern classification

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Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence 2007年第2期20卷 173-179页

作者： Hu, Jing Yang, Jing Gao, Jun Laboratory of Image Information Processing School of Computer and Information Hefei University of Technology Hefei 230009 China Center for Biomimetic Sensing and Control Research Institute of Intelligent Machines Chinese Academy of Sciences Hefei 230031 China

An ordination-fuzzy min-max neural network (OFMM) based on non-metric multidimensional scaling (MDS) is proposed to solve the classification problems of unlabelled input pattern. Firstly, all the input patterns are sorted by MDS to get their similarity measures. Then these measures are used to supervise the following expansion and contraction stage of hyperboxes for classification. OFMM shows the improvements in the validity of unlabelled patterns classification, the network structure, and training time. The experimental results on standard dataset demonstrate that OFMM is a practical and effective classifier which is superior to the traditional general-fuzzy min -max neural network (GFMM).

关键词： Classifiers Fuzzy min-max neural network (FMMN) Multidimensional scaling (MDS) Similarity measures Unlabelled pattern

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LANDING AN UNMANNED AIR VEHICLE: VISION BASED MOTION ESTIMATION AND NONLINEAR CONTROL

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Asian Journal of Control 2008年第3期1卷

作者： Omid Shakernia Yi Ma T. John Koo Shankar Sastry Dept. of Electrical Engineering & Computer Science University of California at Berkeley Berkeley CA94720-1774 U.S.A. Tak-Kuen John Koo received the B.Eng. degree in 1992 in Electronic Engineering and the M.Phil. in 1994 in Information Engineering both from the Chinese University of Hong Kong. From 1994 to 1995 he was a graduate student in Signal and Image Processing Institute at the University of Southern California. He is currently a Ph.D. Candidate in Electrical Engineering and Computer Sciences at the University of California at Berkeley. His research interests include nonlinear control theory hybrid systems inertial navigation systems with applications to unmanned aerial vehicles. He received the Distinguished M.Phil. Thesis Award of the Faculty of Engineering The Chinese University of Hong Kong in 1994. He was a consultant of SRI International in 1998. Currently he is the team leader of the Berkeley AeRobot Team and a delegate of The Graduate Assembly University of California at Berkeley. He is a student member of IEEE and SIAM. S. Shankar Sastry received his Ph.D. degree in 1981 from the University of California Berkeley. He was on the faculty of MIT from 1980-82 and Harvard University as a Gordon McKay professor in 1994. He is currently a Professor of Electrical Engineering and Computer Sciences and Bioengineering and Director of the Electronics Research Laboratory at Berkeley. He has held visiting appointments at the Australian National University Canberra the University of Rome Scuola Normale and University of Pisa the CNRS laboratory LAAS in Toulouse (poste rouge) and as a Vinton Hayes Visiting fellow at the Center for Intelligent Control Systems at MIT. His areas of research are nonlinear and adaptive control robotic telesurgery control of hybrid systems and biological motor control. He is a coauthor (with M. Bodson) of “Adaptive Control: Stability Convergence and Robustness Prentice Hall 1989.” and (with R. Murray and Z. Li) of “A Mathematical Introduction to Robotic Manipulati

In this paper, we use computer vision as a feedback sensor in a control loop for landing an unmanned air vehicle (UAV) on a landing pad. The vision problem we address here is then a special case of the classic ego-motion estimation problem since all feature points lie on a planar surface (the landing pad). We study together the discrete and differential versions of the ego-motion estimation, in order to obtain both position and velocity of the UAV relative to the landing pad. After briefly reviewing existing algorithm for the discrete case, we present, in a unified geometric framework, a new estimation scheme for solving the differential case. We further show how the obtained algorithms enable the vision sensor to be placed in the feedback loop as a state observer for landing control. These algorithms are linear, numerically robust, and computationally inexpensive hence suitable for real-time implementation. We present a thorough performance evaluation of the motion estimation algorithms under varying levels of image measurement noise, altitudes of the camera above the landing pad, and different camera motions relative to the landing pad. A landing controller is then designed for a full dynamic model of the UAV. Using geometric nonlinear control theory, the dynamics of the UAV are decoupled into an inner system and outer system. The proposed control scheme is then based on the differential flatness of the outer system. For the overall closed-loop system, conditions are provided under which exponential stability can be guaranteed. In the closed-loop system, the controller is tightly coupled with the vision based state estimation and the only auxiliary sensor are accelerometers for measuring acceleration of the UAV. Finally, we show through simulation results that the designed vision-in-the-loop controller generates stable landing maneuvers even for large levels of image measurement noise. Experiments on a real UAV will be presented in future work.

关键词： Motion estimation computer vision based control unmanned air vehicle differential flatness

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Twin and scratch detection and removal in micrograph images of inconel™718

Twin and scratch detection and removal in micrograph images ...

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Machine Vision Applications in Industrial Inspection XIV

作者： Jakob, Gerhard Rinnhofer, Alfred Bischof, Horst Benesova, Wanda Joanneum Research Institute of Digital Image Processing Wastiangasse 6 8045 Graz Austria Technical University Graz Institute for Computer Graphics and Vision Inffeldgasse 16/II 8010 Graz Austria

ISBN: (纸本)0819461105

Grain size of forged nickel alloy is an important feature for the mechanical properties of the material. For fully automatic grain size evaluation in images of micrographs it is necessary to detect the boundaries of each grain. This grain boundary detection is influenced directly by artifacts like scratches and twins. Twins can be seen as parallel lines inside one grain, whereas a scratch can be identified as a sequence of collinear line segments that can be spread over the whole image. Both kinds of artifacts introduce artificial boundaries inside grains. To avoid wrong grain size evaluation, it is necessary to remove these artifacts prior to the size evaluation process. For the generation of boundary images various algorithms have been tested. The most stable results were achieved by grayscale reconstruction and a subsequent watershed segmentation. A modified line Hough transform with a third dimension in the Hough accumulator space, describing the distance of the parallel lines, is used to directly detect twins. Scratch detection is done by applying the standard line Hough transform followed by a rule based segment detection along the found Hough lines. The results of these operations give a detection rate of more than 90 percent for twins and more than 50 percent for scratches. © 2006 SPIE-IS&T.

关键词： Object recognition

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Generic Visualization and Manipulation Framework for Three-Dimensional Medical Environments

Generic Visualization and Manipulation Framework for Three-D...

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Annual IEEE Symposium on computer-Based Medical Systems

作者： B.S. Tani T. Nobrega T.R. dos Santos A. von Wangenheim Computer Graphics and Image Processing Laboratory Federal University of Santa Catarina Brazil

We present a visualization and manipulation framework for three-dimensional medical environments. The MVC architecture of the. framework allows the use of different API technologies such us OpenGL and DirectSD, different model representations, and controllers. This facilitates the implementation of medical applications offering resources commonly needed by such systems. Two case studies are shown as examples of such applications

关键词： Biomedical imaging Data visualization computer architecture Engines Medical control systems computer displays Medical services Biomedical equipment Application software Solid modeling

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A Low Complexity Wavelet-Packets Based image Signature for Indexing and Retrieval

A Low Complexity Wavelet-Packets Based Image Signature for I...

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Canadian Conference on Electrical and computer Engineering (CCECE)

作者： Saif Zahir Computer Science Department UNBC Image Processing Multimedia and Graphics Laboratory Prince George BC Canada

image signature is a signal that is significantly shorter and simpler than the image signal and can be used to efficiently identify the original image. Generating an image signature that accurately represents an image can only be done by means of extracting specific features of the image and express it in an efficient way. In this paper, a new wavelet-packets-based (WP) image signature that can be used for image indexing and fast retrieval is proposed. This signature is generated via encoding the position of specific points of the signal that represents some statistics of the wavelet packets coefficients in the various frequency bands. computer simulation results using nearly 7500 image signatures show that the targeted image signature was identified with a very high accuracy in a very short period of time. Also, it showed that the proposed signature is robust against geometric transformation

关键词： Wavelet packets Indexing image retrieval Signal processing image generation Feature extraction Signal generators Encoding Statistics Frequency

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Using context and a genetic algorithm for knowledge-assisted image analysis

Using context and a genetic algorithm for knowledge-assisted...

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1st International Conference on Semantic and Digital Media Technologies, SAMT 2006

作者： Dasiopoulou, Stamatia Papadopoulos, George Th. Mylonas, Phivos Avrtihis, Yannis Kompatsiaris, Ioannis Information Processing Laboratory Electrical and Computer Engineering Department Aristotle University Thessaloniki Greece Informatics and Telematics Institute Centre for Research and Technology Greece Image Video and Multimedia Laboratory School of Electrical and Computer Engineering National Technical University Athens Greece

In this poster, we present an approach to contex-tualized semantic image annotation as an optimization problem. Ontologies are used to capture general and contextual knowledge of the domain considered, and a genetic algorithm is applied to realize the final annotation. Experiments with images from the beach vacation domain demonstrate the performance of the proposed approach and illustrate the added value of utilizing contextual information.

关键词： Ontology

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