Nonparametric pattern recognition systems based on learning a sample decomposition by its dimension

The technique of multilevel nonparametric pattern recognition systems synthesis on the basis of learning sample decomposition principles and parallel computing technology is proposed. This basis provides effective processing of highly dimensional information. Aleksandr Vasil’evich Lapko was born in 1949 and graduated from Frunze Polytechnic Institute in 1971. He has been a doctor of technical sciences since 1990 and a leading researcher at the Institute of Computational Modeling of the Siberian Branch of the Russian Academy of Sciences. His scientific interests include the following: nonparametric statistics, pattern recognition systems, and the design and optimization of indefinite systems. He is the author of 223 publications, including 13 monographs. He is chairman of the Krasnoyarsk regional department of the Pattern Recognition and Image Analysis Association and an Honored Science Worker of the Russian Federation. Vasilii Aleksandrovich Lapko was born in 1974 and graduated from Krasnoyarsk State Technical University in 1996. He has been a doctor of technical sciences since 2004 in systems analysis, management, and information processing. He is a senior researcher at the Institute of Computational Modeling of the Siberian Branch of the Russian Academy of Sciences. His scientific interests include the following: nonparametric statistics, pattern recognition systems, the design of indefinite systems, and collective evaluation methods. He is the author of 105 publications, including 4 monographs. He was awarded by the Russia Academy of Sciences a medal for the best scientific publication in 2005 for young scientists in Informatics, Computer Engineering, and Automation.     

Synthesising surface matching algorithms using the correspondence framework

Computer vision tasks such as registration, modeling and object recognition, are becoming increasingly useful in industry. Each of these applications employs correspondence algorithms to compute accurate mappings between partially overlapping surfaces. In industry, it is essential to select an appropriate correspondence algorithm for a given surface matching task. A correspondence framework has recently been proposed to assist in the selection and creation of correspondence algorithms for these tasks. This paper demonstrates how to use the correspondence framework to create a new surface matching algorithm, which uses stages of an existing model matching algorithm. The efficiency with which the new algorithm is created using the correspondence frame work is emphasized. In addition, results show that the new algorithm is both robust and efficient. The text was submitted by the authors in English. Birgit Maria Planitz, born in 1978, received B. Engineering (Hons) degree at the Queensland University of Technology (QUT) in Brisbane, Australia (2001). Dr. Planitz then continued her studies at QUT, enrolling in a PhD. The PhD was in the field of computer vision, specializing in three-dimensional surface matching. Dr. Planitz graduated from her postraduate degree in 2005, with two major journal publications, six conference papers and a technical report. She is currently working for the e-Health Research Centre/CSIRO ICT Centre. Dr. Planitz is a member of the Australia Pattern Recognition Society. Anthony John Maeder, born 1958, graduated with B. Science (Hons) from University of Witwatersrand in 1980 and M. Science from the University of Natal in 1982. He was awarded his PhD in 1992 by Monash University. Dr. Maeder is currently the Research Director, E-Health Research Centre/CSIRO ICT Centre and Adjunct Professor, Faculty of Health Sciences, University of Queensland. His research areas include digital image processing, image and video compression, medical imaging, computer graphics and visualization. Dr. Maeder has 200 publications consisting of 10 monographs and proceedings, 20 journal papers and 180 conference papers. He is a fellow of the Institution of Engineers Australia; a member of IEEE, ACM, ACS, HISA; a member of SPIE International Technical Committee for Medical Imaging; and a member of national executive committee of the Australian Pattern Recognition Society. John Alan Williams, born in 1973, was awarded his PhD from the Queensland University of Technology (QUT), Australia, in 2001. He was previously awarded undergraduate degrees in Electronic Engineering and Information Technology (Hons), also from QUT, in 1995. He is currently employed at the School of ITEE at The University of Queensland, Brisbane, Australia, where he holds the position of Research Fellow. Dr. William’s research interests include reconfigurable computing and realtime embedded systems, as well as 3D computer vision and imaging. He has authored 5 refereed journal publications and more than 20 refereed conference publications, and has recently edited the Proceedings of the 2004 IEEE International Conference on Field Programmable Technology. He has been a member of the IEEE for eight years.     

Compression of 3D digital signals based on hierarchical grid interpolation

A method of hierarchical compression of 3D digital signals is considered as a generalization of the familiar image compression method based on the hierarchical grid interpolation to the 3D case. Special attention is paid to constructing 3D interpolation systems. The effectiveness of the method developed here is compared to the prototype using only 2D dependences. Bavrina Alina Yur’evna (b. 1980) graduated from the Samara State Aerospace University (SSAU) in 2003. At present, she is a research scholar at the department of Geoinformatics, SSAU. Her scientific interests include image processing, compression, and geoinformatics. A.Yu.Bavrina has more than 10 publications, including 4 articles. She is a member of the Russian Association for Image Recognition and Analysis. Gashnikov Mikhail Valer’evich (b. 1975) graduated from the Samara State Aerospace University (SSAU) in 1998. In 2004, he defended his Ph.D. (Eng.) thesis. At present, M.V. Gashnikov is an associate lecturer at the Department of Geoinformatics (SSAU). The scope of his scientific interests includes image processing, compression, and statistical coding. He has 30 publications, including 12 papers and a monograph (with coauthors). M.V. Gashnikov is a member of the Russian Association for Image Recognition and Analysis. Sergeyev Vladislav Viktorovich (b. 1951) graduated from the Kuibyshev Aviation Institute (now Samara State Aerospace University) in 1974. In 1993, he defended his D.Sc. (eng.) thesis. At present, V.V. Sergeyev heads the Laboratory of Mathematical Methods of Image Processing at the Institute of Image Processing Systems, Russian Academy of Sciences. His scientific interests include digital signal processing, image analysis, image recognition, and geoinformatics. He has more than 150 publications, including 40 articles and two monographs (with co-authors). V.V. Sergeyev is the chairman of the Povolzh’e Division of the Russian Association for Pattern Recognition and Image Analysis and a Corresponding Member of the Russian Academy of Ecology and the Academy of Engineering Sciences of the Russian Federation, a member of SPIE (International Society for Optical Engineering), and a winner of the Samara Regional Award in Science and Engineering.     

Mobile geoinformation systems

The geoinformation system for handheld computers (PDAs) makes it possible to solve a wide range of geographically dispersed data (GDD) processing problems. The developed program software (PS) based on effective models and GDD processing methods has allowed significantly increasing the GDD processing rate in PDAs. Yurii G. Vasin was born in 1940 and graduated from Gor’kii State University in 1962. He received his doctoral (doctor of science) degree in 1988 and was a recipient of the Prize of the Council of Ministers of the USSR in 1990. He is the director of the Research Institute of Applied Mathematics and Cybernetics of the State University of Nizhni Novgorod. His scientific interests include theoretical and applied computer science, pattern recognition and image processing, and information technologies, and he is the author of more than 100 publications. Sergei V. Zherzdev was born in 1976 and graduated from the State University of Nizhni Novgorod in 1999. He is a software engineer at the Research Institute of Applied Mathematics and Cybernetics of the State University of Nizhni Novgorod. His scientific interests include theoretical and applied computer science and data compression techniques. He is the author of 7 publications. Andrei A. Egorov was born in 1982 and graduated from the State University of Nizhni Novgorod in 2006. He is a programmer at the Research Institute of Applied Mathematics and Cybernetics of the State University of Nizhni Novgorod, and his scientific interests include hierarchical structures of data storage on mobile platforms. He is the author of 2 inventions and 7 publications.     

Family of models for describing one class of metallographic images

Models for images syntax are developed, tried, and tested in describing the syntax of microstructural metallographic images of wrought aluminum alloys. Gennadii Mikhailovich Tsibul’skii was born in 1947 and graduated from Krasnoyarsk Polytechnic Institute in 1973. Since 1975, he has been involved in the analysis of digital images. In 1978, he completed his postgraduate course at the Lenin Leningrad Electronic Technical Institute. He received his candidate’s degree in 1987 and a doctoral degree in engineering in 2006. He was appointed a professor in 2007. In 1996, he founded the Artificial Intelligence Systems Department and has worked there as a chairman since then. His scientific interests include the multiagent approach to images analysis, and he is the author of more than 70 publications (including one book published by the Siberian Branch of the Russian Academy of Sciences). At present, Gennadii Tsibul’skii is the director of the Space and Information Technologies Institute at Krasnoyarsk Siberian Federal University. Yurii Anatol’evich Maglinest was born 1965 and graduated from Krasnoyarsk Polytechnic Institute in 1973; he then pursued postgraduate studies there. He received his candidate’s degree in engineering in 1996 in the analysis of metallographic images. He is an associate professor at the State Commission for Academic Degrees and Titles of the Russian Federation. At present, he is a chair of the Scientific University Laboratory of Flexible Software Systems at the Artificial Intelligence Systems Department at Krasnoyarsk Siberian Federal University. His scientific interests include aerospace information storage, processing and analysis, and flexible software systems. Dmitrii Al’bertovich Perfil’ev was born in 1968 and graduated from Krasnoyarsk Polytechnic Institute in 1992. Since 2000, he has been specializing in problems in digital images analysis and, in particular, in describing microstructural pictures of aluminum alloys. He received his candidate’s degree in engineering in 2007 and is the author of 8 publications related to the problem in question. At present, he is a researcher and a lecturer at the Artificial Intelligence Systems Department at Krasnoyarsk Siberian Federal University.     

Automatic updating of raster images of electronic charts

The technology being developed to ensure remote updating of raster images of electronic charts (EC) via global data transmission networks is considered. Implemented methods are presented that can automatically synchronize EC with minimal requirements for the channel capacity. The constructed version of the control system and similar general-purpose software are compared in terms of their efficiency in detecting the differences in graphic data for BIG images. Yurii G. Vasin was born in 1940 and graduated from Gorki State University in 1962. He received his doctoral (doctor of science) degree in 1988, is a member of the Russian Academy of Natural Sciences, and a recipient of the Prize of the Council of Ministers of the USSR in 1990. At present, he is the director of the Research Institute of Applied Mathematics and Cybernetics of the State University of Nizhni Novgorod. His scientific interests include theoretical and applied computer science, pattern recognition and image processing, and information technologies. He is the author of more than 100 publications. Sergei V. Zherzdev was born in 1976 and graduated from the State University of Nizhni Novgorod in 1999. At present, he is a postgraduate student at the same university and a software engineer at the Research Institute of Applied Mathematics and Cybernetics of the State University of Nizhni Novgorod. His scientific interests include theoretical and applied computer science and data compression techniques. He is the author of 7 publications. Evgenii S. Sorokin was born in 1985 and graduated from the Lobachevskii State University of Nizhni Novgorod from the Computational Mathematics and Cybernetics Department in 2007 and the Economics Department in 2008. At present, he is a postgraduate student at the Intellectual Information Systems and Geoinformatics Department. His scientific interests include applied computer science, geoinformation systems, and efficient data processing techniques. He is the author of 6 publications.     

Fully automated segmentation of multiple sclerosis lesions in multispectral MRI

This paper addresses segmentation of multiple sclerosis lesions in multispectral 3-D brain MRI data. For this purpose, we propose a novel fully automated segmentation framework based on probabilistic boosting trees, which is a recently introduced strategy for supervised learning. By using the context of a voxel to be classified and its transformation to an overcomplete set of Haar-like features, it is possible to capture class specific characteristics despite the well-known drawbacks of MR imaging. By successively selecting and combining the most discriminative features during ensemble boosting within a tree structure, the overall procedure is able to learn a discriminative model for voxel classification in terms of posterior probabilities. The final segmentation is obtained after refining the preliminary result by stochastic relaxation and a standard level set approach. A quantitative evaluation within a leave-one-out validation shows the applicability of the proposed method. The text was submitted by the authors in English. Michael Wels was born in 1979 and graduated with a degree in computer science from the University of Wuerzburg in 2006. Currently, he is a member of the research staff at the University of Erlangen-Nuremberg’s Institute of Pattern Recognition working towards his Ph.D. His research interests are medical imaging in general and the application of machine learning techniques to medical image segmentation. Martin Huber studied at the University of Karlsruhe and received his Ph.D. degree in computer science in 1999. Since 1996, he has been with Siemens Corporate Technology and Siemens Medical Solutions. He currently is technical coordinator of the EU funded project Health-e-Child with research interests in medical imaging and semantic data integration. Joachim Hornegger graduated with a degree in computer science (1992) and received his Ph.D. in applied computer science (1996) at the University of Erlangen-Nuremberg (Germany). His Ph.D. thesis was on statistical learning, recognition, and pose estimation of 3-D objects. Joachim was a visiting scholar and lecturer at Stanford University (Stanford, CA, USA) during the 1997–1998 academic year, and, in 2007–2008, he was a visiting professor at Stanford’s Radiological Science Laboratory. In 1998, he joined Siemens Medical Solutions Inc., where he was working on 3D angiography. In parallel with his responsibilities in industry, he was a lecturer at the Universities of Erlangen (1998–1999), Eichstaett-Ingolstadt (2000), and Mannheim (2000–2003). In 2003, Joachim became Professor of Medical Imaging Processing at the University of Erlangen-Nuremberg, and, since 2005, he has been a chaired professor heading the Institute of Pattern Recognition. His main research topics are currently pattern recognition methods in medicine and sports.     

Research in moisture transport through one and two-layered porous composites

1 Introduction Moisture transport or general mass transport is a typical phenomenon that widely exists in porous ma- terials such as soil, construction or porous industrial materials. It is always accompanied with heat transfer and also a?ects temperature variation. A good under- standing of the mechanism of moisture transport pro- cesses is very important in various science areas such as soil science and agriculture, construction industry and chemical engineering[1]. For example, a success- f…     

Fast training for object recognition with structure-from-motion

In this paper we present a system for statistical object classification and localization that applies a simplified image acquisition process for the learning phase. Instead of using complex setups to take training images in known poses, which is very time-consuming and not possible for some objects, we use a handheld camera. The pose parameters of objects in all training frames that are necessary for creating the object models are determined using a structure-from-motion algorithm. The local feature vectors we use are derived from wavelet multiresolution analysis. We model the object area as a function of 3D transformations and introduce a background model. Experiments made on a real data set taken with a handheld camera with more than 2500 images show that it is possible to obtain good classification and localization rates using this fast image acquisition method. The text was submitted by the authors in English. Marcin Grzegorzek, born in 1977, obtained his Master’s Degree in Engineering from the Silesian University of Technology Gliwice (Poland) in 2002. Since December 2002 he has been a PhD candidate and member of the research staff of the Chair for Pattern Recognition at the University of Erlangen-Nuremberg, Germany. His fields are 3D object recognition, statistical modeling, and computer vision. He is an author or coauthor of seven publications. Michael Reinhold, born in 1969, obtained his degree in Electrical Engineering from RWTH Aachen University, Germany, in 1998. Later, he received a Doctor of Engineering from the University of Erlangen-Nuremberg, Germany, in 2003. His research interests are statistical modeling, object recognition, and computer vision. He is currently a development engineer at Rohde & Schwarz in Munich, Germany, where he works in the Center of Competence for Digital Signal Processing. He is an author or coauthor of 11 publications. Ingo Scholz, born in 1975, graduated in computer science at the University of Erlangen-Nuremberg, Germany, in 2000 with a degree in Engineering. Since 2001 he has been working as a research staff member at the Institute for Pattern Recognition of the University of Erlangen-Nuremberg. His main research focuses on the reconstruction of light field models, camera calibration techniques, and structure from motion. He is an author or coauthor of ten publications and member of the German Gesellschaft für Informatik (GI). Heinrich Niemann obtained his Electrical Engineering degree and Doctor of Engineering degree from Hannover Technical University, Germany. He worked with the Fraunhofer Institut für Informationsverarbeitung in Technik und Biologie, Karlsruhe, and with the Fachhochschule Giessen in the Department of Electrical Engineering. Since 1975 he has been a professor of computer science at the University of Erlangen-Nuremberg, where he was dean of the engineering faculty of the university from 1979 to 1981. From 1988 to 2000, he was head of the Knowledge Processing research group at the Bavarian Research Institute for Knowledge-Based Systems (FORWISS). Since 1998, he has been a spokesman for a “special research area” with the name of “Model-Based Analysis and Visualization of Complex Scenes and Sensor Data” funded by the German Research Foundation. His fields of research are speech and image understanding and the application of artificial intelligence techniques in these areas. He is on the editorial board of Signal Processing, Pattern Recognition Letters, Pattern Recognition and Image Analysis, and the Journal of Computing and Information Technology. He is an author or coauthor of seven books and about 400 journal and conference contributions, as well as editor or coeditor of 24 proceedings volumes and special issues. He is a member of DAGM, ISCA, EURASIP, GI, IEEE, and VDE and an IAPR fellow.     

Design and performance analysis of tracking controller for uncertain nonlinear composite system using neural networks

Based on high order dynamic neural network, this paper presents the tracking problem for uncertain nonlinear composite system, which contains external disturbance, whose nonlinearities are assumed to be unknown. A smooth controller is designed to guarantee a uniform ultimate boundedness property for the tracking error and all other signals in the dosed loop. Certain measures are utilized to test its performance. No a priori knowledge of an upper bound on the “optimal” weight and modeling error is required; the weights of neural networks are updated on-line. Numerical simulations performed on a simple example illustrate and clarify the approach.     

Some results of analysis of the group classification problem

The coordinate representation of classifications is proposed for solving the group synthesis problem. Theorems on the existence of group classifications, which are closer to true classification than other classifications, are formulated. The concept of (ε, δ) stability of classification algorithms is defined, and the stability of the algorithms of crisp and fuzzy group classifications is estimated. Aidarkhanov Makhmet Berkutbaevich (b. 1948). In 1971, graduated from the Kazakh State University, Department of Mechanics and Mathematics (Alma-Ata). In 1977, completed postgraduate studies at the Computer Center of the USSR Academy of Sciences (Moscow) and defended Cand. Sci. thesis in Mathematical Cybernetics. In 1983–84, was on doctorate probation at the same Computer Center and in 1993 defended the Dr. Sci. thesis (Phys-Math). Works at the Institute of Problems of Informatics and Control, MON RK; as a director since 1994. Fields of interest: development of mathematical models of recognition and classification and with the data processing, analysis and transmission. Published 129 works, including 3 monographs. Academician of the International Academy of Information Sciences and International Academy of Natural and Social Sciences (Moscow Branch). Since 2000, a member of UNESCO ACCESS-net (international association supporting stable development of computer centers and institutions of information technologies) representing Kazakhstan. In 2004, awarded the medal “For Merits in the Development of Science in the Kazakh Republic.” Lira L’vovna La (b. 1962). In 1984 graduated from Kazakh State University, Department of Mechanics and Mathematics (Alma-Atai). In 1986, defended Cand. Sci. thesis (Phys-Math) (specialty 05.13.16-application of computers, simulation, and mathematical methods in scientific research). Works as a leading scientist at the Institute of Problems of Informatics and Control, MON RK. Fields of interest: development of mathematical models of pattern recognition and classification and analysis of group classification synthesis for unfuzzy and fuzzy classifications (about 30 publications).     

Stabilization of the compressed data formation rate in hierarchical image compression

A new method is proposed for stabilizing the rate of compressed data formation in the case of hierarchical image compression. The method is based on using various values of the control parameter (maximum error) for various scale levels of image representation and for error correction at the last level depending on the received compressed information content. Gashnikov Mikhail Valer’evich (b. 1975) graduated from the Samara State Aerospace University (SSAU) in 1998. In 2004, he defended his Ph.D. thesis in engineering. At present, Gashnikov is an associate lecturer at the Department of Geoinformatics (SSAU). The scope of his scientific interests includes image processing, compression, and statistical coding. He has 30 publications, including 12 papers and a monograph (with co-authors). He is a member of the Russian Association for Image Recognition and Analysis. Glumov Nikolai Ivanovich (b. 1962) graduated from the Kuibyshev Aviation Institute (now Samara State Aerospace University) in 1985. In 1994, he defended his Ph.D. thesis in engineering. At present, Glumov is a senior scientist at the Institute of Image Processing Systems, Russian Academy of Sciences. His scientific interests include image processing and recognition, image compression, and simulation of digital image formation systems. He has more than 60 publications, including 20 articles and a monograph (with co-authors). He is a member of the Russian Association of Image Recognition and Analysis. Sergeyev Vladislav Viktorovich (b. 1951) graduated from the Kuibyshev Aviation Institute (now Samara State Aerospace University) in 1974. In 1993, he defended his D.Sc. (eng.) thesis. At present, V.V. Sergeyev heads the Laboratory of Mathematical Methods of Image Processing at the Institute of Image Processing Systems, Russian Academy of Sciences. His scientific interests include digital signal processing, image analysis, image recognition, and geoinformatics. He has more than 150 publications, including 40 articles and two monographs (with co-authors). V.V. Sergeyev is the chairman of the Povolzh’e Division of the Russian Association for Pattern Recognition and Image Analysis and a Corresponding Member of the Russian Academy of Ecology and the Academy of Engineering Sciences of the Russian Federation, a member of SPIE (International Society for Optical Engineering), and a winner of the Samara Regional Award in Science and Engineering.     

A vector model of associative memory with clipped synapses

The influence of a clipping procedure on the properties of vector associative memory is investigated. The analysis is performed for the particular case of a phase model of a parametric neural network with 2q-state neurons. The critical network size N _c is found. It is shown that, for small network sizes (N < N _c ), the clipping leads to an increase of the storage capacity and enhances the network ability to retrieve strongly distorted patterns. Clipping of bigger networks (N > N _c ) leads to a deterioration of the recognition ability and reduces the storage capacity. Boris Vladimirovich Kryzhanovsky was born in 1950 in Yasnaya Polyana in the Tula region of Russia and graduated (with an M.Sc.) from Yerevan State University in 1971. He received his Ph.D. (Optics) in 1981 and his D.Sc. (Laser Physics) in 1991. At the present time, he is the director of the Center for Optical Neural Technologies of the Scientific Research Institute for Systems Analysis of the Russian Academy of Sciences. His research interests include neural networks. He is a corresponding member of the Russian Academy of Sciences and the author of over 200 research publications. Vladimir Mikhailovich Kryzhanovsky was born in 1984 in Kirovakan, Armenia and graduated (with an M.Sc.) from the Moscow Engineering Physics Institute in 2007. At the present time, he is a junior research assistant at the Center for Optical Neural Technologies of the Scientific Research Institute for Systems Analysis of the Russian Academy of Sciences. His research interests include Neural Networks, and he is the author of over 20 research publications. Dina Igorevna Simkina was born 1981 in Buinaksk in Dagestan, Russia and graduated (with an M.Sc.) from Dagestan State University in 2003. At the present time, she is a junior research assistant at the Center for Optical Neural Technologies of the Scientific Research Institute for Systems Analysis of the Russian Academy of Sciences. Her research interests include neural networks, and she is the author of over 20 research publications.     

Statistical methods for detecting latent periodicity patterns in biological sequences: The case of small-size samples

An original spectral-statistical approach for detecting latent periodicity in biological sequences is proposed. This approach can be applied under conditions of limited statistical sample. It allows one to avoid redundancy and instability when identifying the latent periodicity structure. The optimality of the periodicity-pattern-size estimates obtained for approximate tandem repeats on the basis of the spectral-statistical approach is demonstrated in practical examples. Maria Borisovna Chaley was born in 1963 and graduated from the Moscow Institute of Physics and Technology in 1988 (M.Sc.). She received her PhD in biophysics in 1993 and became a docent in bioinformatics in 2003. At the present time, she is a senior research fellow at the Institute of Mathematical Biology Problems of the Russian Academy of Sciences. Her research interests include bioinformatics, genetic text analysis, and molecular evolution. She is the author of over 40 research publications, including 14 journal articles. Nafisa Nailovna Nazipova was born in 1960 and graduated from the Department of Computational Mathematics and Cybernetics of Lenin Kazan State University in 1982 (MSc.). She received her PhD in physics and mathematics (mathematic modeling, numerical methods, and program complexes) in 2002. At the present time, she is the head of the bioinformatics laboratory at the Institute of Mathematical Biology Problems of the Russian Academy of Sciences. Her research interests include bioinformatics and the structural and functional organization of genetic sequences. She is the author of over 35 research publications, including 9 papers in refereed journals and 2 book chapters. Vladimir Andreyevich Kutyrkin was born in 1952 and graduated from the Department of Mechanics and Mathematics of Lomonosov Moscow State University in 1974 (MSc.). He received his PhD in physics and mathematics in 1995. At the present time, he is a docent of Bauman Moscow State Technical University. His research interests include applied mathematical statistics, computational and discrete mathematics, and bioinformatics. He is the author of over 20 research publications, including 11 journal papers.     

Contour tracking on ultrasound sequences of vascular images

Simple vascular measurements on sequences of echographic images can be used to quantify important indexes of cardiovascular risk. The measurement of the intima-media thickness and the characterization of the endothelial function are but two examples. Real-time image processing systems would be helpful to automatically track, locate, and discriminate vascular structures through image sequences. Many algorithms have been developed to accomplish this task and they are generally based on the application of a mathematical operator at the points of a starting contour and on an iterative procedure that brings the starting contour to the final contour. In this paper, the performances of a mathematical operator that exploits both temporal and spatial information are compared to those of an operator that only exploits spatial information. The paper shows that, in general, when tracking contours on image sequences and when two or more gray-level discontinuities are present and close to each other, as in the case of arteries, both operators should be used in sequence. The text was submitted by the authors in English. Marcello Demi was born in Cecina, Italy, in 1956. He graduated in Electronic Engineering from the University of Florence, Italy in 1985. He is currently head of the Computer Vision Group at the CNR Institute of Clinical Physiology in Pisa and he teaches a course on Medical Image Processing at the faculty of Applied Physics, University of Pisa. His research interests are image processing systems and filtering schemes inspired by the early stages of biological vision systems. He has 80 scientific publications and his objective is the development of common projects with people who work in the area of biological vision for the purpose of both understanding biological vision and developing image processing systems. Elisabetta Bianchini was born in Lucca, Italy, in 1975. She received the degree in Electronic Engineering from the University of Pisa, Italy, in 2004. Since 2004 she is junior research at CNR, the Italian National Research Council, at the DSP lab in IFC (Institute of Clinical Physiology). Her field of interest is image processing and in particular development of methods for the assessment of indices of cardiovascular risk from ultrasound images. She is author or co-author of 14 scientific publications in international journals and conference proceedings. Francesco Faita was born in 1973 in La Spezia (Italy). In 2001 he graduated from Università degli Studi di Pisa obtaining the degree of Electronic Engineer. Since 2001, he has been working as a research fellow at the Institute of Clinical Physiology of the Italian National Research Council. His main research interests lie in Computer Vision, in particular in the field of ultrasound image motion estimation. A major focus of his research in the last years has been development of clinically applicable automated techniques for cardiovascular analysis and prediction of disease progression. He is author or co-author of 58 scientific publications in international journals and conference proceedings. Viencenzo Gemignani was born in 1969, in Viareggio (Italy). In 1995, he graduated in Electronic Engineering from the University of Pisa. Since 1996, he has been working at the Institute of Clinical Physiology of the Italian National Research Council. His main research interests are in diagnostic ultrasound, realtime image analysis and non-invasive patient monitoring systems. He teaches a course on DSP processors at the Faculty of Engineering, University of Pisa. He is author or coauthor of 40 scientific publications in international journals and conference proceedings and is co-inventor of 4 patents in the field of ultrasonic image processing.     

Program and research system for the recognition of individuals by photos of faces on documents

In this article, the developed program and research system for recognizing individuals based on photos of faces on documents is described. The requirements for the system made at the development stage are determined. The most important of these requirements is the possibility of carrying out the investigations of various algorithms with the purpose of the comparing their efficiency, determining their optimum parameters, and selecting the best system of signs. The composition and destination of the main components of the program and research system for recognizing individuals are presented. The main feature of implementation is the use of processing scenarios in the system performed by an interpreter and the presence of an expanded set of the elemental processing functions. This solution results in the rapid development and variation of various algorithms of image processing, formation of signs, and classification. Evgenii Valer’evich Myasnikov. Born in 1981. In 2004, he graduated the Samara State Aerospace University (SGAU) and entered the Post-Graduate Education of SGAU. In 2007, Myasnikov defended the Candidate of Science (Engineering) Dissertation. Currently, he works as the Probationer Researcher at the Image Processing Systems Institute, Russian Academy of Sciences and simultaneously as the Assistant of the Department of Geoinformatics at SCAU. The circle of scientific interests involves the creation of software complexes, image processing, and pattern recognition, and search for images in databases. Myasnikov has 23 publications, including six articles. He is the member of the Russian Association of Pattern Recognition and Image Processing. Vladislav Viktorovich Sergeev. Born in 1951. In 1974, he graduated the Kuibyshev Aviation Institute (now, the Samara State Aerospace University). In 1994, he defended his Doctor of Science (Engineering) dissertation. Currently, he works at the Chief of Laboratory of Mathematical Methods of Image Processing at the Image Processing Systems Institute, Russian Academy of Sciences. The circle of scientific interests involves the digital processing of signals, analysis of images, pattern recognition, and geoinformatics. Sergeev has more than 200 publications, including about 40 articles and two monographs (in partnership). Sergeev is the Chairman of the Povolzh’e Division of the Russian Association of Pattern Recognition and Image Analysis. He is the Corresponding Member of the Russian Environmental Academy and Academy of the Engineering Sciences of the Russian Federation, the member of SPIE (the International Society for Optical Engineering), and the Laureate of the Samara Regional Prize in the Field of Science and Engineering. Nikolai Ivanovich Glumov. Born in 1962. In 1985, he graduated the Kuibyshev Aviation Institute (now, the Samara State Aerospace University). In 1994, he defended the Candidate of Science (Engineering) Dissertation. Currently, he is working as the Senior Researcher at the Image Processing Institute, Russian Academy of Sciences. The circle of scientific interests involves the image processing and pattern recognition, image compression, and simulation of the systems of formation of digital images. Glumov has more than 90 publications, including more than 30 articles and one monograph (in partnership). He is the member of the Russian Association of Pattern Recognition and Image Processing. Aleksandr Pavlovich Chikhonadskikh. Born in 1959. In 1981, he graduated the Mozhaiskii Military Space Engineering Institute (now, the Mozhaiskii Military Space Engineering Academy). In 1988, he defended the Candidate of Science (Engineering) Dissertation. Currently, he is working as the Chief of the Second Research Department at the FGUP State Research Institute of Applied Problems. The circle of scientific interests involves the creation of program-apparatus complexes, digital processing of signals, analysis of images, and pattern recognition. Chikhonadskikh has more than 70 publications, including three articles and three monographs (two in partnership). Aleksandr Viktorovich Koryakin. Born in 1959. In 1982, he graduated the Dnepropetrovsk State University. In 2002, he defended the Doctor of Science (Engineering) Dissertation. Currently, he is working as the Senior Researcher at the FGUP State Research Institute of Applied Problems. The circle of scientific interests involves the digital processing of signals, analysis of images, pattern recognition, and creation of software-apparatus complexes. Koryakin has more than 100 publications, including three monographs. Inga Yur’evna Terent’eva. Born in 1978. In 2000, she graduated the North-West Academy of State Service (St. Petersburg), and in 2004, she graduated the Post-Graduate Education at the Institute of the Human Brain, Russian Academy of Sciences. In 2005, she defended the Candidate of Science (Psychological) Dissertation. Currently, she is working as the Chief of Laboratory at the FGUP State Research Institute of Applied Problems. The circle of scientific interests involves the analysis of biometric data, pattern recognition, and neural networks. Terent’eva has 23 publications, including eight articles.     

ARMiner: A Data Mining Tool Based on Association Rules

In this paper,ARM iner,a data mining tool based on association rules,is introduced.Beginning with the system architecture,the characteristics and functions are discussed in details,including data transfer,concept hierarchy generalization,mining rules with negative items and the re-development of the system.An example of the tool‘s application is also shown.Finally,Some issues for future research are presented.     

A new algebraic modelling approach to distributed problem-solving in MAS

This paper is devtoed to a new algebraic modelling approach to distributed problem-solving in multi-agent systems(MAS),which is featured by a unified framework for describing and treating social behaviors,social dynamics and social intelligence.A coneptual architecture of algebraic modelling is presented.The algebraic modelling of typical social be-haviors,social situation and social dynamics is discussed in the context of distributed problem-solving in MAS .The comparison and simulation on distributed task allocations and resource assignments in MAS show more advantages of the algebraic approach than other conventional methods.     

Tunes on the table

The Music Table is an augmented reality system for composing music by manipulating objects on a tabletop as a physicalized representation of the music being heard. Educational theory, and the apparent success of related applications in various learning contexts, seems to support this idea. In our experiments with children, all were able to make a musical pattern and made many changes to their pattern over a short period of time. We propose its suitability as an educational tool, particularly in short and intense interactive learning situations such as children's museums. We discuss some future developments of the idea. Rodney Berry was born in 1963 in Australia. He came to ATR in 1999. He is musician, composer and media artist, he gained a Master of fine arts from the UNSW college of Fine Arts in Sydney in 1999. He is currently completing a Ph.D. at UTS Creativity and Cognition Studios in Sydney while continuing to work at ATR. Mao Makino was born in Osaka, graduated from School of Literature, Arts and Cultural Studies. She has been a Media Creator at ATR since 1999. Her 3D animations featured in the MIDAS interactive dance system shown at the exhibition “Dream Technologies for the 21st Century” in Tokyo in 2000. Naoto Hikawa was born in Sendai. He graduated in “Visual concept planning” from Osaka University of the Arts. Since 1998, he has been a media creator with ATR. He was also involved in the production of ATR MIC lab's MIDAS dance system. He is also a VJ at nightclubs in Osaka. Dr Masami Suzuki was born in Tokyo, Japan. He obtained Master degree from Keio University in 1980, since then has worked for KDD (currently KDDI) telecommunication company. His research area has been spread from natural language processing to creative human interfaces. Currently, he is a chief researcher at ATR Media Information Science Laboratories. Dr. Naomi Inoue was born in Nara, Japan. He gained Master degree and Ph.D. from Kyoto University in 1984 and 1998, respectively. His research interests are natural language processing, speech recognition and graphics user interface for mobile phones. Currently, he is a group leader at ATR Media Information Science Laboratories.     

A comparison between the first absolute central moment and the gradient of Gaussian when detecting edges in presence of Gaussian noise

The gray-level absolute central moment of the first order provides ridges at gray-level discontinuities as well as a conventional gradient of Gaussian (GoG). A mass center b of the gray-level variability can also be associated to the first absolute central moment. When given a starting point p, vector b indicates the path which joins p to the nearest gray-level discontinuity as well as the gradient of the GoG magnitude. However, when the right configuration of the operator is chosen, vector b indicates a point which is closer to the discontinuity than p, regardless of the distance between p and the discontinuity. Therefore, when using vector b, gray-level discontinuities can be located with an iterative approach. In this paper, the edge detection properties of the first absolute central moment are compared with those of the GoG. The text was submitted by the authors in English. Marcello Demi was born in Cecina, Italy, in 1956. He graduated in electronic engineering from the University of Florence, Italy, in 1985. He is currently head of the Computer Vision Group at the CNR Institute of Clinical Physiology in Pisa, senior scientist for ESAOTE Spa, and he teaches a course on medical image processing at the Faculty of Applied Physics, University of Pisa. His research interests are cardiovascular image processing systems, contour tracking of moving deformable objects, and filtering schemes inspired by the early stages of biological vision systems. He has 80 scientific publications and his objective is the development of common projects with people who work in the area of biological vision for the purpose of both understanding biological vision and developing image processing systems. Francesco Faita was born on June 28, 1973, in La Spezia (Italy). In 2001, he graduated from Universitá degli Studi di Pisa obtaining the degree of electronic engineer. Since 2001, he has been working as a research fellow at the Institute of Clinical Physiology of the Italian National Research Council. His main research interests lie in computer vision, in particular, in the field of ultrasound imaging. Elisabetta Bianchini was born in Lucca, Italy, in 1975. She received her degree in electronic engineering from the University of Pisa, Italy, in 2004. Since 2004, she has been a junior researcher at CNR, the Italian National Research Council, at the DSP lab in IFC (Institute of Clinical Physiology). Vincenzo Gemignani was born on October 10, 1969, in Viareggio (Italy). In 1995, he graduated from Universitá degli Studi di Pisa obtaining the degree of electronic engineer. Since 1996, he has been working as a research fellow at the Institute of Clinical Physiology of the Italian National Research Council. His main research interests lie in digital signal processing, in particular, in field real-time image analysis.