Automatic computation of left ventricle ejection fraction from dynamic ultrasound images

Left Ventricle (LV) Ejection Fraction (EF) is a fundamental parameter for heart function assessment. Being based on border tracing, however, manual computation of EF is time-consuming and extremely prone to inter-and intraobserver variability. In this paper we present an automatic method for EF computation which provides results in agreement with those provided by expert observers. The segmentation strategy consists of two stages: first, the region of interest is identified by means of mimetic criteria; then, the identified region is used for initialization of an active contour based on a variational formulation of level set methods, which provides accurate segmentation of the LV cavity. Volume calculation is then performed according to the conventional Simpson’s rule and, finally, the EF is computed. The text was submitted by the authors in English. Umberto Barcaro is an Associate Professor at the Computer Science Department of Pisa University and an Associate Researcher at the Signals and Images Laboratory of the Institute of Information Science and Technologies of the National Research Council. He teaches Physics and Computer Science Laboratory at the Faculty of Pharmacy, and Signal Theory at the Faculty of Sciences. His research activity regards the automatic analysis of signals and images of clinical interest. In particular, he has studied spontaneous and evoked electroencephalographic and polygraphic signals, and ultrasound images. Davide Moroni (Magenta, 1977), M.Sc. in Mathematics honours degree from the University of Pisa in 2001, dipl. at the Scuola Normale Superiore of Pisa in 2002, PhD in Mathematics at the University of Rome “La Sapienza” in 2006, is a research fellow at the Institute of Information Science and Technologies of the Italian National Research Council, in Pisa. His main interests include geometric modeling, computational topology, image processing and medical imaging. At present he is involved in a number of European research projects working in discrete geometry and dynamic scene analysis. Ovidio Salvetti, director of research at the Institute of Information Science and Technologies (ISTI) of the Italian National Research Council (CNR), in Pisa, is working in the field of theoretical and applied computer vision. His fields of research are image analysis and understanding, pictorial information systems, spatial modeling, and intelligent processes in computer vision. He is a coauthor of four books and monographs and more than three hundred technical and scientific articles; he also possesses ten patents regarding systems and software tools for image processing. He has been a scientific coordinator of several national and European research and industrial projects, in collaboration with Italian and foreign research groups, in the fields of computer vision and high-performance computing for diagnostic imaging. He is member of the editorial boards of the international journals Pattern Recognition and Image Analysis and G. Ronchi Foundation Acts. He is at present the CNR contact person in ERCIM (the European Research Consortium for Informatics and Mathematics) for the Working Group on Vision and Image Understanding, member of IEEE and of the steering committee of a number of EU projects. He is head of the ISTI Signals and Images Laboratory.     

Microembolic signal characterization by transcranial Doppler imaging

Transcranial Doppler detection and monitoring of cerebral microemboli have provided a new and useful method to diagnose, and potentially to foresee, increased risk of stroke. Until now, however, the assessment of this method in routine clinical practice has been limited by the lack of a reliable automatic differentiation between solid and gaseous microemboli. The aim of this work is the definition of a clinical diagnostic support procedure for the automatic recognition of emboli of different composition. The proposed method makes use of image processing techniques and neural algorithms for data interpretation and performs a feature-based analysis of the ultrasonographic images showing the microembolic events. Application to clinical cases selected by expert neurologists for their clinical relevance and experimental results have showed effective operability of the developed procedure. The text was submitted by the authors in English. Sara Colantonio, MS honors degree in Computer Science from the University of Pisa in 2004, PhD student in information engineering at the Dept. of Information Engineering, University of Pisa, is a research fellow at the Institute of Information Science and Technologies of the Italian National Research Council, in Pisa. She has a grant from Finmeccanica for studies in the field of image categorization with applications in medicine and quality control. Her main interests include neural networks, machine learning, industrial diagnostics, and medical imaging. She is coauthor of more than fifteen scientific papers. At present, she is involved in a number of European research projects regarding image mining, information technology, and medical decision support systems. Ovidio Salvetti, director of research at the Institute of information science and Technologies (ISTI) of the Italian National Research Council (CNR), in Pisa, is working in the field of theoretical and applied computer vision. His fields of research are image analysis and understanding, pictorial information systems, spatial modeling, and intelligent processes in computer vision. He is coauthor of four books and monographs and more than three hundred technical and scientific articles; he is also owner of ten patents regarding systems and software tools for image processing. He has been scientific coordinator of several national and European research and industrial projects in the fields of computer vision and high-performance computing for diagnostic imaging. He is member of the editorial boards of the international journals Pattern Recognition and Image Analysis and G. Ronchi Foundation Acts. He is at present the CNR contact person in ERCIM (the European Research Consortium for Informatics and Mathematics) for the Working Group on “Vision and Image Understanding,” member of IEEE and of the steering committee of a number of EU projects. He is head of the ISTI Signals and Images Laboratory.     

Cell image analysis ontology

Cell image analysis in microscopy is the core activity of cytology and cytopathology for assessing cell physiological (cellular structure and function) and pathological properties. Biologists usually make evaluations by visually and qualitatively inspecting microscopic images: this way, they are particularly able to recognize deviations from normality. Nevertheless, automated analysis is strongly preferable for obtaining objective, quantitative, detailed, and reproducible measurements, i.e., features, of cells. Yet, the organization and standardization of the wide domain of features used in cytometry is still a matter of challenging research. In this paper, we present the Cell Image Analysis Ontology (CIAO), which we are developing for structuring the cell image features domain. CIAO is a structured ontology that relates different cell parts or whole cells, microscopic images, and cytometric features. Such an ontology has incalculable value since it could be used for standardizing cell image analysis terminology and features definition. It could also be suitably integrated into the development of tools for supporting biologists and clinicians in their analysis processes and for implementing automated diagnostic systems. Thus, we also present a tool developed for using CIAO in the diagnosis of hematopoietic diseases. The text was submitted by the authors in English. Sara Colantonio. MSc degree with honors in computer science, University of Pisa, 2004; PhD student in information engineering at the Department of Information Engineering, Pisa University; research fellow at the Institute of Information Science and Technologies, National Research Council, Pisa. Received a grant from Finmeccanica for studies in the field of image categorization with applications in medicine and quality control. Her main interests include neural networks, machine learning, industrial diagnostics, and medical imaging. Coauthor of more than 30 scientific papers. Currently involved in a number of European research projects regarding image mining, information technology, and medical decision support systems. Igor B. Gurevich. Born 1938. Dr. Eng. (Diploma Engineer (Automatic Control and Electrical Engineering), 1961, Moscow Power Engineering Institute, Moscow, USSR); Dr. (Theoretical Computer Science/Mathematical Cybernetics), 1975, Moscow Institute of Physics and Technology, Moscow, USSR. Head of department at the Dorodnicyn Computing Center of the Russian Academy of Sciences, Moscow; assistant professor at the Faculty of Computer Science, Moscow State University. Since 1960, has worked as an engineer and researcher in industry, medicine, and universities and in the Russian Academy of Sciences. Area of expertise: image analysis; image understanding; mathematical theory of pattern recognition; theoretical computer science; pattern recognition and image analysis techniques for applications in medicine, nondestructive testing, and process control; knowledge bases; knowledge-based systems. Two monographs (in coauthorship); 135 papers on pattern recognition, image analysis, and theoretical computer science and applications in peer-reviewed international and Russian journals and conference and workshop proceedings; one patent of the USSR and four patents of the RF. Executive secretary of the Russian Association for Pattern Recognition and Image Analysis, member of the governing board of the International Association for Pattern Recognition (representative from the Russian Federation), IAPR fellow. Has served as PI of many research and development projects as part of national research (applied and basic) programs of the Russian Academy of Sciences, the Ministry of Education and Science of the Russian Federation, the Russian Foundation for Basic Research, the Soros Foundation, and INTAS. Deputy editor in chief of Pattern Recognition and Image Analysis. Massimo Martinelli. Works at the Institute of Information Science and Technologies (ISTI), National Research Council (CNR), Pisa. Member of the W3C multimedia semantics incubator group; coordinator of the CNR-ISTI web systems group. His main interests include semantic web and web technologies. Coauthor of more than 50 scientific papers. Currently involved in a number of European research projects regarding semantic web, information technology, multimedia semantics, and medical decision support systems. Ovidio Salvetti. Director of research at the Institute of Information Science and Technologies (ISTI), National Research Council (CNR), Pisa. Working in the field of theoretical and applied computer vision. His fields of research are image analysis and understanding, pictorial information systems, spatial modeling, and intelligent processes in computer vision. Coauthor of four books and monographs and more than 300 technical and scientific articles, with ten patents regarding systems and software tools for image processing. Has served as a scientific coordinator of several national and European research and industrial projects, in collaboration with Italian and foreign research groups, in the fields of computer vision and high-performance computing for diagnostic imaging. Member of the editorial boards of the international journals Pattern Recognition and Image Analysis and G. Ronchi Foundation Acts. Currently the CNR contact person in ERCIM (the European Research Consortium for Informatics and Mathematics) for the Working Group on Vision and Image Understanding and a member of IEEE and of the steering committee of a number of EU projects. Head of the ISTI Signals and Images Laboratory. Yulia O. Trusova. Born 1980. Graduated from the Faculty of Computational Mathematics and Cybernetics of Lomonosov Moscow State University in 2002. Works at the Dorodnicyn Computing Center of the Russian Academy of Sciences. Scientific interests: mathematical theory of pattern recognition and image analysis, methods of discrete mathematics, databases and knowledge bases, and computational linguistics. Coauthor of more than 25 papers. Laureate of the Aspirant Award, 2003–2005. Member of the Russian Association for Pattern Recognition and Image Analysis.     

A two-step approach for automatic microscopic image segmentation using fuzzy clustering and neural discrimination

The early diagnosis of lymphatic system tumors heavily relies on the computerized morphological analysis of blood cells in microscopic specimen images. Automating this analysis necessarily requires an accurate segmentation of the cells themselves. In this paper, we propose a robust method for the automatic segmentation of microscopic images. Cell segmentation is achieved following a coarse-to-fine approach, which primarily consists in the rough identification of the blood cell and, then, in the refinement of the nucleus contours by means of a neural model. The method proposed has been applied to different case studies, revealing its actual feasibility. This article was submitted by the authors in English. Sara Colantonio, M. Sc. honors degree in Computer Science from the University of Pisa in 2004, PhD student in Information Engineering at the Dept. of Information Engineering, Pisa University, is a research fellow at the Institute of Information Science and Technologies of the Italian National Research Council, in Pisa. She has a grant from Finmeccanica for studies in the field of image categorization with applications in medicine and quality control. Her main interests include neural networks, machine learning, industrial diagnostics, and medical imaging. She is a coauthor of more than fifteen scientific papers. At present, she is involved in a number of European research projects regarding image mining, information technology, and medical decision support systems. Ovidio Salvetti, director of research at the Institute of Information Science and Technologies (ISTI) of the Italian National Research Council (CNR), in Pisa, is working in the field of theoretical and applied computer vision. His fields of research are image analysis and understanding, pictorial information systems, spatial modeling, and intelligent processes in computer vision. He is a coauthor of four books and monographs and more than three hundred technical and scientific articles; he also possesses ten patents regarding systems and software tools for image processing. He has been a scientific coordinator of several national and European research and industrial projects, in collaboration with Italian and foreign research groups, in the fields of computer vision and high-performance computing for diagnostic imaging. He is member of the editorial boards of the international journals Pattern Recognition and Image Analysis and G. Ronchi Foundation Acts. He is at present the CNR contact person in ERCIM (the European Research Consortium for Informatics and Mathematics) for the Working Group on Vision and Image Understanding, member of IEEE and of the steering committee of a number of EU projects. He is head of the ISTI Signals and Images Laboratory. Igor B. Gurevich. Born 1938. Dr. Eng. [Diploma Engineer (Automatic Control and Electrical Engineering), 1961, Moscow Power Engineering Institute, Moscow, USSR]; Dr. (Theoretical Computer Science/Mathematical Cybernetics), 1975, Moscow Institute of Physics and Technology, Moscow, USSR. Head of department at the Dorodnicyn Computing Center of the Russian Academy of Sciences, Moscow; assistant professor at the Computer Science Faculty, Moscow State University. He has worked from 1960 to present as an engineer and researcher in industry, medicine, and universities and in the Russian Academy of Sciences. Area of expertise: image analysis, image understanding, mathematical theory of pattern recognition, theoretical computer science, pattern recognition and image analysis techniques for applications in medicine, nondestructive testing, process control, knowledge bases, knowledge-based systems. Two monographs (in coauthorship), 135 papers on pattern recognition, image analysis, theoretical computer science and applications in peer reviewed international and Russian journals, conference and workshop proceedings; one patent of the USSR, four patents of the RF Executive Secretary of the Russian Federation Association for Pattern Recognition and Image Analysis, member of the International Association for Pattern Recognition Governing Board (representative from the Russian Federation), IAPR fellow. He has been the PI of many research and development projects as part of national research (applied and basic research) programs of the Russian Academy of Sciences, of the Ministry of Education and Science of the Russian Federation, of the Russian Foundation for Basic Research, of the Soros Foundation, and of INTAS. Vice Editor-in-Chief of Pattern Recognition and Image Analysis, International Academic Publishing Company “Nauka/Interperiodica” Pleiades Publishing.     

Object tracking in video-surveillance

This paper faces the automatic object tracking problem in a video-surveillance task. A previously selected and then identified target has to be retrieved in the scene under investigation because it is lost due to masking, occlusions, or quick and unexpected movements. A two-step procedure is used, firstly motion detection is used to determine a candidate target in the scene, secondly using a semantic categorization and Content Based Image Retrieval techniques, the candidate target is identified whether it is the one that was lost or not. The use of Content Based Image Retrieval serves as support to the search problem and is performed using a reference data base which was populated a priori. The article is published in the original. Davide Moroni (Magenta, 1977), M.Sc. in Mathematics honours degree from the University of Pisa in 2001, dipl. at the Scuola Normale Superiore of Pisa in 2002, Ph.D. in Mathematics at the University of Rome ‘La Sapienza’ in 2006, is a research fellow at the Institute of Information Science and Technologies of the Italian National Research Council, in Pisa. His main interests include geometric modelling, computational topology, image processing and medical imaging. At present he is involved in a number of European research projects working in discrete geometry and scene analysis. Gabriele Pieri (Pescia, 1974), M.Sc. (2000) in Computer Science from the University of Pisa, since 2001 joined the “Signals and Images” Laboratory at ISTI-CNR, Pisa, working in the field of image analysis. His main interests include neural networks, machine learning, industrial diagnostics and medical imaging. He is author of more than twenty papers.     

On condensed representations of constrained frequent patterns

Constrained frequent patterns and closed frequent patterns are two paradigms aimed at reducing the set of extracted patterns to a smaller, more interesting, subset. Although a lot of work has been done with both these paradigms, there is still confusion around the mining problem obtained by joining closed and constrained frequent patterns in a unique framework. In this paper, we shed light on this problem by providing a formal definition and a thorough characterisation. We also study computational issues and show how to combine the most recent results in both paradigms, providing a very efficient algorithm that exploits the two requirements (satisfying constraints and being closed) together at mining time in order to reduce the computation as much as possible. Francesco Bonchi received his Ph.D. in computer science from the University of Pisa in December 2003, with the thesis “Frequent Pattern Queries: Language and Optimizations”. Currently, he is a postdoc at the Institute of Information Science and Technologies (ISTI) of the Italian National Research Council in Pisa, where he is a member of the Knowledge Discovery and Delivery Laboratory. He has been a visiting fellow at the Kanwal Rekhi School of Information Technology, Indian Institute of Technology, Bombay (2000, 2001). His current research interests are data mining query language and Optimization, frequent pattern mining, privacy-preserving data mining, bioinformatics. He is one of the teachers of a course on data mining held at the faculty of Economics at the University of Pisa. He served as a referee at various national and international conferences on databases, data mining, logic programming and artificial intelligence. Claudio Lucchese received the Master Degree in Computer Science summa cum laude from Ca' Foscari University of Venice in October 2003. He is currently a Ph.D. student at the same university and Research Associate at the Institute of Information Science and Technologies (ISTI) of the Italian National Research Council in Pisa, where he is a member of the High Performance Computing Laboratory. He is mainly interested in frequent pattern mining, privacy-preserving data mining, and data mining techniques for information retrieval.     

Fast correction of bleed-through distortion in grayscale documents by a blind source separation technique

Ancient documents are usually degraded by the presence of strong background artifacts. These are often caused by the so-called bleed-through effect, a pattern that interferes with the main text due to seeping of ink from the reverse side. A similar effect, called show-through and due to the nonperfect opacity of the paper, may appear in scans of even modern, well-preserved documents. These degradations must be removed to improve human or automatic readability. For this purpose, when a color scan of the document is available, we have shown that a simplified linear pattern overlapping model allows us to use very fast blind source separation techniques. This approach, however, cannot be applied to grayscale scans. This is a serious limitation, since many collections in our libraries and archives are now only available as grayscale scans or microfilms. We propose here a new model for bleed-through in grayscale document images, based on the availability of the recto and verso pages, and show that blind source separation can be successfully applied in this case too. Some experiments with real-ancient documents arepresented and described. Anna Tonazzini graduated cum laude in Mathematics from the University of Pisa, Italy, in 1981. In 1984 she joined the Istituto di Scienza e Tecnologie dell'Informazione of the Italian National Research Council (CNR) in Pisa, where she is currently a researcher at the Signals and Images Laboratory. She cooperated in special programs for basic and applied research on image processing and computer vision, and is co-author of over 60 scientific papers. Her present interest is on inverse problems theory, image restoration and reconstruction, document analysis and recognition, independent component analysis, neural networks and learning. Emanuele Salerno graduated in Electronic Engineering from the University of Pisa, Italy, in 1985. In September 1987 he joined the Italian National Research Council (CNR) at the Department of Signal and Image Processing, Information Processing Institute (now Institute of Information Science and Technologies, ISTI, Signals and Images Laboratory), Pisa, Italy, where he has been working in applied inverse problems, image reconstruction and restoration, microwave nondestructive evaluation, and blind signal separation. He has been assuming different responsibilities in research programs in nondesctructive testing, robotics, numerical models for image reconstruction and computer vision, neural networks techniques in astrophysical imagery. At present, he is local scientific responsible in the framework of the European Space Agency's “Planck Surveyor Satellite” mission, and takes part in the European CRAFT project “ISyReADeT”, for document image restoration. Luigi Bedini graduated cum laude in Electronic Engineering from the University of Pisa, Italy, in 1968. Since 1970 he has been a Researcher of the Italian National Research Council, Istituto di Scienza e Tecnologie dell'Informazione, Pisa, Italy. His interests have been in modelling, identification, and parameter estimation of biological systems applied to non-invasive diagnostic techniques. At present, his research interest is in the field of digital signal processing, image reconstruction and neural networks applied to image processing. He is co-author of more than 80 scientific papers. From 1971 to 1989, he was Associate Professor of System Theory at the Computer Science Department, University of Pisa, Italy.     

Defect detection in <Emphasis Type="Italic">C</Emphasis>-scan maps

An image processing procedure is proposed aiming at detecting porosity defects in materials used in the aerospace industry by means of the analysis of C-scan images obtained by ultrasound inspection techniques. An image is described through a number of features, which are then analyzed in order to evaluate the similarity of an unknown examined image with respect to a set of reference ones. The procedure is performed by firstly applying a 2D wavelet transform of the input image and then a feature extraction process based on statistics of the detail images produced by the transform itself. A data reduction process is then applied using the principal component analysis technique. Finally, a decision ruler performs the image classification by considering the distance among points in the principal component plane. An application of this procedure is presented for the analysis of single images, giving a localization of defects. Preliminary results, obtained by processing both simulation images and real C-scan maps, showed the efficacy of the proposed method. The text was submitted by the authors in English. Edoardo Bozzi (Pisa, 1943) is a researcher at ISTI-CNR, in Pisa, working in the field of signal processing. His main interests include design of hardware and software instrumentation and development of noninvasive diagnostic techniques for medical and industrial applications. He is author of about a hundred technical papers. Maria Grazia Di Bono (Potenza, 1975), MS (2002) in computer science from the University of Pisa, has been performing since March 2003 her research activity at ISTI-CNR, in Pisa, working in the field of computer vision. Her main interests include neural networks, multimedia processing, and web systems for industrial and medical applications. She is coauthor of more than twelve technical and scientific papers. G. Cavaccini. Born in Naples October 28, 1959. Graduate in physics. From 1986 to 1988, he was at the Naples Section of the National Institute for Nuclear Physics, where he participated in research on gamma spectroscopy. From 1988 to 1999, he was at Aerodynamics Institute U. Nobile of the University of Naples with a scholarship on “Nondestructive Testing of Aeronautical Structures by Means of Thermographics Techniques.” From 1989 to the present date, he has been an Alenia Aeronautica employee. Up to 2001, he worked as an NDI technologist and researcher at the Engineering Materials, Processes, and NDI Dept. He holds ASNT Level III, Italian Aerospace NDT Board, and JAR 145 NDT certifications. He is involved also as coordinator in national and European research programs; since 2001, he has been responsible for nondestructive testing and structural health monitoring, as well as for the exploitation of technology research. Massimo Chimenti (Altopascio, 1946) is a senior scientist at ISTI-CNR, in Pisa, working in the field of signal and image formation, acquisition, and preprocessing. His scientific interests range from hardware design to software systems with specific applications in medical imaging. He is author of more than two hundred technical and scientific papers and he is also owner of more than ten patents regarding hardware and software systems for image acquisition and processing. He is at present leader of a research group in the Signals and Images Lab of ISTI working in the field of microwave and ultrasound imaging. Ovidio Salvetti, Director of Research at the Institute of Information Science and Technologies (ISTI) of the Italian National Research Council (CNR), in Pisa, is working in the field of theoretical and applied computer vision. His fields of research are image analysis and understanding, pictorial information systems, spatial modeling, and intelligent processes in computer vision. He is coauthor of four books and monographs and more than three hundred technical and scientific articles; he is also owner of ten patents regarding systems and software tools for image processing. He has been scientific coordinator of several national and European research and industrial projects, in collaboration with Italian and foreign research groups, in the fields of computer vision and high-performance computing for diagnostic imaging. He is member of the editorial boards of the international journals Pattern Recognition and Image Analysis and G. Ronchi Foundation Acts. He is at present the CNR contact person in ERCIM (the European Research Consortium for Informatics and Mathematics) for the Working Group on “Vision and Image Understanding,” member of IEEE and of the steering committee of a number of EU projects. He is head of the ISTI Signals and Images Laboratory.     

Real time object detection and tracking through a robotized system

The task of detection and tracking of a moving object is addressed. Algorithms have been developed which perform this task for monitoring and surveillance purposes. Prediction is also implemented in the algorithm to locate the target in order to keep it stationary in the center of the image, resolve events of occlusion or masking, and increase normal tracking performance. Real-time implementation generates deformation in target appearance, and then a shape database is used to improve these situations when a target is lost. A prototypical system has been developed that makes use of a moving camera located on a robotized system. A case study is presented about animal tracking in infrared live video. The text was submitted by the authors in English. Gabriele Pieri (Pescia, 1974), M. Sci. (2000) in Computer Science from the University of Pisa, since 2001 joined the “Signals and Images” Laboratory at ISTI-CNR, Pisa, working in the field of image analysis. His main interests include neural networks, machine learning, industrial diagnostics, and medical imaging. He is the author of more than 20 papers. Marco Benvenuti, born in 1965. He received a degree in electronic engineering from the University of Florence in 1994. Since 2002 he is director of the Research and Development Department of TD Group Spa. Since 1994 he has been a researcher and consultant of the National Research Council of Italy and he collaborates with various private and public research institutions, charged as scientifically responsible of several national and international research projects. His fields of activity are related to data and image processing and integration for environmental monitoring and analysis, satellite and airborne remote sensing techniques, and GIS applications. He has long experience in international projects management and was reviewer of the EU Commission as an expert on remote sensing and GIS applications. He has been a consultant to certain international organizations, such as the World Meteorological Organisation, F.A.O., and the European Space Agency. In 1996 he was the Italian delegate to the Thematic Cooperation Group on Operational Environment Monitoring of the DG-XII (EC). He has publications and communications at the national and international level. Edoardo Carnier, born in 1962. He received a degree in computer science from the University of Pisa in 1997, developing a thesis on high order perceptron initialization. He has strong experience in image processing and analysis. He participated in the design and development of the SIRIO infrared system for environmental monitoring and, in particular, contributed to the study and implementation of tracking and motion detection algorithms. Since February 1998 he has been on the staff of the research and development group of TD Group Spa as project manager on a research project to realize an integrated system based on infrared technology for video-surveillance applications. Ovidio Salvetti, Director of Research at the Institute of Information Science and Technologies (ISTI) of the Italian National research Council (CNR), in Pisa, is working in the field of theoretical and applied computer vision. His fields of research are image analysis and understanding, pictorial information systems, spatial modeling and intelligent processes in computer vision. He is co-author of four books and monographs and more than three hundreds technical and scientific articles; he is also owner of ten patents regarding systems and software tools for image processing. He has been scientific coordinator of several National and European research and industrial projects, in collaboration with Italian and foreign research groups, in the fields of computer vision and high-performance computing for diagnostic imaging. He is member of the Editorial Boards of the International Journals Pattern Recognition and Image Analysis and G. Ronchi Foundation Acts. He is at present the CNR Contact Person in ERCIM (the European Research Consortium for Informatics and Mathematics) for the Working Group on “Vision and Image Understanding,” Member of IEEE and of the Steering Committee of a number of EU Projects. He is Head of the ISTI “Signals and Images” Laboratory.     

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.     

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.     

Hiding a halftone secret image in two camouflaged halftone images

In this paper, we shall propose a method to hide a halftone secret image into two other camouflaged halftone images. In our method, we adjust the gray-level image pixel value to fit the pixel values of the secret image and two camouflaged images. Then, we use the halftone technique to transform the secret image into a secret halftone image. After that, we make two camouflaged halftone images at the same time out of the two camouflaged images and the secret halftone image. After overlaying the two camouflaged halftone images, the secret halftone image can be revealed by using our eyes. The experimental results included in this paper show that our method is very practicable. The text was submitted by the authors in English. Wei-Liang Tai received his BS degree in Computer Science in 2002 from Tamkang University, Tamsui, Taiwan, and his MS degree in Computer Science and Information Engineering in 2004 from National Chung Cheng University, Chiayi, Taiwan. He is currently a PhD student of Computer Science and Information Engineering at National Chung Cheng University. His research fields are image hiding, digital watermarking, and image compression. Chi-Shiang Chan received his BS degree in Computer Science in 1999 from National Cheng Chi University, Taipei, Taiwan, and his MS degree in Computer Science and Information Engineering in 2001 from National Chung Cheng University, Chiayi, Taiwan. He is currently a PhD student of Computer Science and Information Engineering at National Chung Cheng University. His research fields are image hiding and image compression. Chin-Chen Chang received his BS degree in Applied Mathematics in 1977 and his MS degree in Computer and Decision Sciences in 1979, both from National Tsing Hua University, Hsinchu, Taiwan. He received his PhD in Computer Engineering in 1982 from National Chiao Tung University, Hsinchu, Taiwan. During the academic years of 1980–1983, he was on the faculty of the Department of Computer Engineering at National Chiao Tung University. From 1983–1989, he was on the faculty of the Institute of Applied Mathematics, National Chung Hsing University, Taichung, Taiwan. From 1989 to 2004, he has worked as a professor in the Institute of Computer Science and Information Engineering at National Chung Cheng University, Chiayi, Taiwan. Since 2005, he has worked as a professor in the Department of Information Engineering and Computer Science at Feng Chia University, Taichung, Taiwan. Dr. Chang is a fellow of the IEEE, a fellow of the IEE, and a member of the Chinese Language Computer Society, the Chinese Institute of Engineers of the Republic of China, and the Phi Tau Phi Society of the Republic of China. His research interests include computer cryptography, data engineering, and image compression.     

Reliability versus cost: Design of a probabilistic broadcast algorithm

Summary We propose a probabilistic algorithm to solve the problem of distributed broadcast. A simple diffusion algorithm is introduced, and its reliability is evaluated. The cost and reliability of the probabilistic algorithm are compared with the corresponding deterministic algorithm. Augusto Ciuffoletti graduated in computer sciences at the University of Pisa in 1980. From 1980 to 1983 he worked with Selenia and OtoMelara on projects funded by the Italian National Research Council. Since 1984 he has been doing research at the University of Pisa, where he is now an assistant. He is particularly interested in studying efficient and reliable low level services (such as broadcast).     

Image hiding scheme using modulus function and optimal substitution table

The simple least-significant-bit (LSB) substitution technique is the easiest way to embed secret data in the host image. To avoid image degradation of the simple LSB substitution technique, Wang et al. proposed a method using the substitution table to process image hiding. Later, Thien and Lin employed the modulus function to solve the same problem. In this paper, the proposed scheme combines the modulus function and the optimal substitution table to improve the quality of the stego-image. Experimental results show that our method can achieve better quality of the stego-image than Thien and Lin’s method does. The text was submitted by the authors in English. Chin-Shiang Chan received his BS degree in Computer Science in 1999 from the National Cheng Chi University, Taipei, Taiwan and the MS degree in Computer Science and Information Engineering in 2001 from the National Chung Cheng University, ChiaYi, Taiwan. He is currently a Ph.D. student in Computer Science and Information Engineering at the National Chung Cheng University, Chiayi, Taiwan. His research fields are image hiding and image compression. Chin-Chen Chang received his BS degree in applied mathematics in 1977 and his MS degree in computer and decision sciences in 1979, both from the National Tsing Hua University, Hsinchu, Taiwan. He received his Ph.D. in computer engineering in 1982 from the National Chiao Tung University, Hsinchu, Taiwan. During the academic years of 1980–1983, he was on the faculty of the Department of Computer Engineering at the National Chiao Tung University. From 1983–1989, he was on the faculty of the Institute of Applied Mathematics, National Chung Hsing University, Taichung, Taiwan. From 1989 to 2004, he has worked as a professor in the Institute of Computer Science and Information Engineering at National Chung Cheng University, Chiayi, Taiwan. Since 2005, he has worked as a professor in the Department of Information Engineering and Computer Science at Feng Chia University, Taichung, Taiwan. Dr. Chang is a Fellow of IEEE, a Fellow of IEE and a member of the Chinese Language Computer Society, the Chinese Institute of Engineers of the Republic of China, and the Phi Tau Phi Society of the Republic of China. His research interests include computer cryptography, data engineering, and image compression. Yu-Chen Hu received his Ph.D. degree in Computer Science and Information Engineering from the Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, Taiwan in 1999. Dr. Hu is currently an assistant professor in the Department of Computer Science and Information Engineering, Providence University, Sha-Lu, Taiwan. He is a member of the SPIE society and a member of the IEEE society. He is also a member of the Phi Tau Phi Society of the Republic of China. His research interests include image and data compression, information hiding, and image processing.     

eInvoke: Secure e-Invoicing based on web services

Electronic Invoicing services (e-Invoicing) will have a pivotal role in all the stages of handling Value Added Tax (VAT) for European Member States. Through a systematic introduction of e-invoicing, tax administrators will be able to implement new tools and procedures to carry out alternative controls that are less intrusive on the trading partners. Nevertheless, successful European e-invoicing implementations need to be in compliance with the corresponding European Directive 2001/115/EC. Most contemporary e-Invoicing implementations are proprietary and based on EDI, thus demonstrating great deficiencies. This paper presents an open electronic invoicing system named eInvoke, based on XML, XML cryptography and Web Services, that addresses all security requirements imposed by the Directive. Our e-Invoicing system has been accepted by “CEN/ISSS e-Invoicing Focus Group on Standards and Developments on electronic invoicing relating to VAT Directive 2001/115/EC” and its summary appears in CEN 2003 report [13] (pg 79–85), forwarded to EC as a recommendation. Alexandros Kaliontzoglou holds a Degree in Electrical and Computer Engineering from the National Technical University of Athens (NTUA), Greece. Since 2001 he is a PhD candidate in the area of Network and Information Systems Security at the Telecommunications laboratory in the School of Electrical and Computer Engineering of NTUA. Since April 2000 he has been working for Expertnet S.A. as a security engineer specializing at Web Technologies and network applications, and he has been active both in European research projects in the 5th and 6th Framework Programme (eMayor, Intelcities, SELIS, Reshen, La Mer, TSEC, WebSig) and projects of the Greek private sector. His research interests focus in the area of IT Security, Service Oriented Architectures, Web Services, Software Engineering, e-Government, e-Commerce and Public Key Infrastructures. Pelagia Boutsi has obtained the Degree in Informatics from the University of Piraeus, Greece, in November 2001. Since April 2002, she is a PhD candidate in the area of Security Information at the Computer Science Department of University of Piraeus. Since September 2001 she is employed at Expertnet S.A. as member of the Technical Department. Her current research interests are in the fields of PKIs, XML and XML Security. She has participated in European research projects and projects of the private sector. Despina Polemi has obtained the Degree in Applied Mathematics from Portland State University(USA) in 1984, Ph.D. in Applied Mathematics (Coding Theory) from City University of New York (Graduate Center) in 1991. She held teaching positions (1984– 1995) in Queens College and Baruch College of City University of NewYork. From 1991 to 1996 was assistant professor (tenure track) in State University of New Yorkat Farmingdale in the department of Mathematics. During 1996–2002 she was an associate researcher in ICCS. From 2000 to 2003 she acted as President of the BoD in a security consulting company Expertnet (www.expertnet.net.gr) and technical manager of the company from 2000–2004. She nowa Professor in the University of Piraeus R&D department. Her current research interests are in the fields of cryptology, security and e-business. She has over ninety publications in the above areas. She has received many research grants from various organizations such as the Danish Research Foundation, MSI Army Research Office/Cornell University, IEEE, State University of New York (SUNY), and The Graduate School of City University of New York (CUNY). She has been project manager (PM)/technical manger (TM) in security projects of various programmes such as National Security Agency (NSA), Dr. Nuala McGann Drescher Foundation, Greek Ministry of Defense, INFOSEC TELEMATICS for Administrations (COSACC), the Fifth Framework IST Programme (HARP, BEE, SEED, WebSig, TSEC, CORAS, RESHEN, SEED, La Mer, SECRETS) and the 6FP (e-Mayor, Intelcities, BIOSEC, SELIS). She participated in the EC security projects of the programs COST, ACTS, and NATOs’ security projects. She is a member of IEEE. She serves as an evaluator, reviewer and expert in the European Commission and consultant for the FP6.     

Life sciences and cyberinfrastructure: Dual and interacting revolutions that will drive future science

Over the past quarter century, two revolutions, one in biomedicine, the other in computing and information technology leading to cyberinfrastructure, have made the largest advances and the most significant impacts on science, technology, and society. The interface between these areas is rich with opportunity for major advances. The Life Sciences Grid Research Group (LSG-RG) of the Global Grid Forum recognized the opportunities and needs to bring the communities together to ensure the cyberinfrastructure will be constructed for the benefit of science. This article gives an overview of the area, the activities of the LSG-RG, and the minisymposium organized by LSG-RG, and introduces the papers in this Special Issue of New Generation Computing. Peter Arzberger, Ph.D.: He is the Director of Life Sciences Initiatives, University of California San Diego; Director of the National Biomedical Computation Resource (http://nbcr.ucsd.edu), funded by the National Center of Research Resource of NIH; and the Chair of the Pacific Rim Application and Grid Middleware Assembly (http://www.pragma-grid.edu), an organization of 20 institutions around the pacific rim whose mission is to establish sustained collaborations and to advance the use of grid technologies in applications. He serves on the US National CODATA Committee and the National Advisory Board of the US Long Term Ecological Research. His hobby is working on Lloyds. Abbas Farazdel, Ph.D.: He is a Senior Scientist and an IT Solution Strategist in the Advanced Technologies unit at the IBM Life Sciences. Previously, Dr. Farazdel worked at several positions in IBM including Cluster System Strategist; Data Warehousing and Data Mining Solutions Implementation Manager; and High Performance Computing Consultant. Abbas is the co-chair of the Global Grid Forum (GGF) Life Sciences Grids Research Group. He serves on the Scientific Board of the European Health Grid and the Mid Hudson Technology Council of New York. Abbas received his Ph.D. in Quantum Chemistry and M.Sc. in Computational Physics from the University of Massachusetts concurrently. Akihiko Konagaya, Dr. Eng.: He is Project Director of Bioinformatics Group, RIKEN Genomic Sciences Center. He received his B.S. and M.S. from Tokyo Institute of Technology in 1978 and 1980 in Informatics Science, and joined NEC Corporation in 1980, Japan Advanced Institute of Science and Technology in 1997, RIKEN GSC in 2003. His research covers wide area from computer architectures to bioinformatics. He has been much involved into the Open Bioinformatics Grid project since 2002. Larry Ang: As the Project Director in the Bioinformatics Institute (BII), he is in charge of major international collaborative projects on biomedical grids between BII and other research organizations (http://web.bii.a-star.edu.sg/ larry/). In particular, he works actively with bodies such as Pragma where he serves on the Steering Committee. He is also the Secretary of the Life Sciences Grid Research Group of GGF (Global Grid Forum) He serves on the Gelato Federation; Gelato was started by HP Labs and pushes open source software on linux platforms. Shinji Shimojo, Ph.D.: He received his M.E. and Ph.D. degrees from Osaka University in 1983 and 1986, respectively. He was an Assistant Professor with the Department of Information and Computer Sciences, Faculty of Engineering Science at Osaka University from 1986, and an Associate Professor with Computation Center from 1991 to 1998. During the period, he also worked as a visiting researcher at the University of California, Irvine for a year. He has been a Professor with Cybermedia Center (then Computation Center) at Osaka University since 1998. His current research work is focusing on a wide variety of multimedia applications, peer-to-peer communication networks, ubiquitous network systems and Grid technologies. He is a member of ACM, IEEE and IEICE. Rick L. Stevens, Ph.D.: He is Professor, University of Chicago; director, Mathematics and Computer Science Division/Argonne National Laboratory; director, ANL/UC Computation Institute; project director for National Science Foundation supported TeraGrid project; head of the Argonne/Chicago Futures Lab. He is interested in the development of innovative tools and techniques that enable computational scientists to solve important large-scale problems effectively on advanced scientific computers. His research focuses on three principal areas: advanced collaboration and visualization environments, high-performance computer architectures (including Grids), and computational problems in life sciences and systems biology. He teaches courses on computer architecture, collaboration technology, virtual reality, parallel computing, and computational science.     

Geometric Analysis of Particle Motion in a Vector Image Field

This paper proposes a geometrical model for the Particle Motion in a Vector Image Field (PMVIF) method. The model introduces a c-evolute to approximate the edge curve in the gray-level image. The c-evolute concept has three major novelties: (1) The locus of Particle Motion in a Vector Image Field (PMVIF) is a c-evolute of image edge curve; (2) A geometrical interpretation is given to the setting of the parameters for the method based on the PMVIF; (3) The gap between the image edge’s critical property and the particle motion equations appeared in PMVIF is padded. Our experimental simulation based on the image gradient field is simple in computing and robust, and can perform well even in situations where high curvature exists. Chenggang Lu received his Bachelor of Science and PhD degrees from Zhejiang University in 1996 and 2003, respectively. Since 2003, he has been with VIA Software (Hang Zhou), Inc. and Huawei Technology, Inc. His research interests include image processing, acoustic signaling processing, and communication engineering. Zheru Chi received his BEng and MEng degrees from Zhejiang University in 1982 and 1985 respectively, and his PhD degree from the University of Sydney in March 1994, all in electrical engineering. Between 1985 and 1989, he was on the Faculty of the Department of Scientific Instruments at Zhejiang University. He worked as a Senior Research Assistant/Research Fellow in the Laboratory for Imaging Science and Engineering at the University of Sydney from April 1993 to January 1995. Since February 1995, he has been with the Hong Kong Polytechnic University, where he is now an Associate Professor in the Department of Electronic and Information Engineering. Since 1997, he has served on the organization or program committees for a number of international conferences. His research interests include image processing, pattern recognition, and computational intelligence. Dr. Chi has authored/co-authored one book and nine book chapters, and published more than 140 technical papers. Gang Chen received his Bachelor of Science degree from Anqing Teachers College in 1983 and his PhD degree in the Department of Applied Mathematics at Zhejiang University in 1994. Between 1994 and 1996, he was a postdoctoral researcher in electrical engineering at Zhejiang University. From 1997 to 1999, he was a visiting researcher in the Institute of Mathematics at the Chinese University of Hong Kong and the Department of Electronic and Information Engineering at The Hong Kong Polytechnic University. Since 2001, he has been a Professor at Zhejiang University. He has been the Director of the Institute of DSP and Software Techniques at Ningbo University since 2002. His research interests include applied mathematics, image processing, fractal geometry, wavelet analysis and computer graphics. Prof. Chen has co-authored one book, co-edited five technical proceedings and published more than 80 technical papers. (David) Dagan Feng received his ME in Electrical Engineering & Computing Science (EECS) from Shanghai JiaoTong University in 1982, MSc in Biocybernetics and Ph.D in Computer Science from the University of California, Los Angeles (UCLA) in 1985 and 1988 respectively. After briefly working as Assistant Professor at the University of California, Riverside, he joined the University of Sydney at the end of 1988, as Lecturer, Senior Lecturer, Reader, Professor and Head of Department of Computer Science/School of Information Technologies, and Associate Dean of Faculty of Science. He is Chair-Professor of Information Technology, Hong Kong Polytechnic University; Honorary Research Consultant, Royal Prince Alfred Hospital, the largest hospital in Australia; Advisory Professor, Shanghai JiaoTong University; Guest Professor, Northwestern Polytechnic University, Northeastern University and Tsinghua University. His research area is Biomedical & Multimedia Information Technology (BMIT). He is the Founder and Director of the BMIT Research Group. He has published over 400 scholarly research papers, pioneered several new research directions, made a number of landmark contributions in his field with significant scientific impact and social benefit, and received the Crump Prize for Excellence in Medical Engineering from USA. More importantly, however, is that many of his research results have been translated into solutions to real-life problems and have made tremendous improvements to the quality of life worldwide. He is a Fellow of ACS, HKIE, IEE, IEEE, and ATSE, Special Area Editor of IEEE Transactions on Information Technology in Biomedicine, and is the current Chairman of IFAC-TC-BIOMED.     

Two fusion predictors for discrete-time linear systems with different types of observations

New fusion predictors for linear dynamic systems with different types of observations are proposed. The fusion predictors are formed by summation of the local Kalman filters/predictors with matrix weights depending only on time instants. The relationship between fusion predictors is established. Then, the accuracy and computational efficiency of the fusion predictors are demonstrated on the first-order Markov process and the GMTI model with multisensor environment. Recommended by Editorial Board member Lucy Y. Pao under the direction of Editor Young Il Lee. This work was partially supported by the Korea Science and Engineering Foundation (KOSEF) grant funded by the Korean government (MOST), No. R01-2007-000-20227-0 and the Center for Distributed Sensor Network at GIST. Ha-Ryong Song received the B.S. degree in Control and Instrumentation Engineering from the Chosun University, Korea, in 2006, the M.S. degree in School of Information and Mechatronics from the Gwangju Institute of Science and Technology, Korea, in 2007. He is currently a Ph.D. candidate in Gwangju Institute of Science and Technology. His research interests include estimation, target tracking systems, data fusion, nonlinear filtering. Moon-Gu Jeon received the B.S. degree in architectural engineering from the Korea University, Korea in 1988. He then received both the M.S. and Ph.D. degrees in computer science and scientific computation from the University of Minnesota in 1999 and 2001, respectively. Currently, he is an Associate Professor at the School of Information and Mechatronics of the Gwangju Institute of Science and Technology (GIST). His current research interests are in machine learning and pattern recognition and evolutionary computation. Tae-Sun Choi received the B.S. degree in Electrical Engineering from the Seoul National University, Seoul, Korea, in 1976, the M.S. degree in Electrical Engineering from the Korea Advanced Institute of Science and Technology, Seoul, Korea, in 1979, and the Ph.D. degree in Electrical Engineering from the State University of New York at Stony Brook, in 1993. He is currently a Professor in the School of Information and Mechatronics at Gwangju Institute of Science and Technology, Korea. His research interests include image processing, machine/robot vision, and visual communications. Vladimir Shin received the B.Sc. and M.Sc. degrees in Applied Mathematics from Moscow State Aviation Institute, in 1977 and 1979, respectively. In 1985 he received the Ph.D. degree in Mathematics at the Institute of Control Science, Russian Academy of Sciences, Moscow. He is currently an Associate Professor at Gwangju Institute of Science and Technology, South Korea. His research interests include estimation, filtering, tracking, data fusion, stochastic control, identification, and other multidimensional data processing methods.     

Clusterization of semantic meanings in the problem of sense equivalence situation recognition

A complex approach to the formalization of sense relations between lexeme and its lexical correlates as precedents of the sense equivalence situations is presented. A technique is outlined for the presentation and usage of information on the lexical meaning of a natural language word to solve the problem of pattern recognition based on standard lexical functions. Dmitriy V. Mikhailov. Born 1974. Graduated from the Yaroslav-the-Wise Novgorod State University, Novgorod, in 1997. From 1997 to 2000, a computer engineer at the Staroruspribor plant in Staraya Russa, Novgorod region. Obtained his PhD (Kandidat Nauk) degree in Physics and Mathematics in 2003. From 2000 to 2007 has worked at the Department of Computer Software of Novgorod State University. Now he is a Docent of the Department of Information Technologies and Systems at the same university. Since 2002 is a member of Russian Association for Pattern Recognition and Image Analysis. Scientific interests: computational linguistics and artificial intelligence. In scientific area of Pattern Recognition and Image Analysis has 18 papers. Gennadii M. Emelyanov. Born 1943. Graduated from the Leningrad Institute of Electrical Engineering in 1966. Obtained his PhD (Kandidat Nauk) and his Doctoral (Doktor Nauk) degrees in 1971 and 1990, respectively. From 1993 to 2003, a Dean of the Faculty of Mathematics and Computer Science at Yaroslavthe-Wise Novgorod State University. Now he is a Professor of the Department of Information Technologies and Systems at the same university. Scientific interests: construction of problem-oriented computing systems of image processing and analysis.