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54 produkter
54 produkter
Inbunden, Engelska, 2006
1 113 kr
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Networks have become nearly ubiquitous and increasingly complex, and their support of modern enterprise environments has become fundamental. Accordingly, robust network management techniques are essential to ensure optimal performance of these networks. This monograph treats the application of numerous graph-theoretic algorithms to a comprehensive analysis of dynamic enterprise networks. Network dynamics analysis yields valuable information about network performance, efficiency, fault prediction, cost optimization, indicators and warnings.Based on many years of applied research of generic network dynamics, this work covers a number of elegant applications (including many new and experimental results) of traditional graph theory algorithms and techniques to computationally tractable network dynamics analysis to motivate network analysts, practitioners and researchers alike. The material is also suitable for graduate courses addressing state-of-the-art applications of graph theory in analysis of dynamic communication networks, dynamic databasing, and knowledge management.
E-bok
PDF, Engelska, 20071 413 kr
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Networks have become nearly ubiquitous and increasingly complex, and their support of modern enterprise environments has become fundamental. Accordingly, robust network management techniques are essential to ensure optimal performance of these networks. This monograph treats the application of numerous graph-theoretic algorithms to a comprehensive analysis of dynamic enterprise networks. Network dynamics analysis yields valuable information about network performance, efficiency, fault prediction, cost optimization, indicators and warnings.
Based on many years of applied research of generic network dynamics, this work covers a number of elegant applications (including many new and experimental results) of traditional graph theory algorithms and techniques to computationally tractable network dynamics analysis to motivate network analysts, practitioners and researchers alike. The material is also suitable for graduate courses addressing state-of-the-art applications of graph theory in analysis of dynamic communication networks, dynamic databasing, and knowledge management.
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PDF, Tyska, 2013447 kr
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E-bok
PDF, Tyska, 2013537 kr
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Häftad, Tyska, 1987
509 kr
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Häftad, Tyska, 1985
459 kr
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Häftad, Engelska, 2006
1 113 kr
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This book constitutes the refereed proceedings of the 7th International Conference on Document Analysis Systems, DAS 2006, held in Nelson, New Zealand, in February 2006. The 33 revised full papers and 22 poster papers presented were carefully reviewed and selected from 78 submissions. The papers are organized in topical sections on digital libraries, image processing, handwriting, document structure and format, tables, language and script identification, systems and performance evaluation, and retrieval and segmentation.
E-bok
PDF, Engelska, 20061 416 kr
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Häftad, Engelska, 2002
544 kr
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Robotics is a highly interdisciplinary research topic, that requires integration of methods for mechanics, control engineering, signal processing, planning, gra- ics, human-computer interaction, real-time systems, applied mathematics, and software engineering to enable construction of fully operational systems. The diversity of topics needed to design, implement, and deploy such systems implies that it is almost impossible for individual teams to provide the needed critical mass for such endeavors. To facilitate interaction and progress on sensor-based intelligent robotics inter-disciplinary workshops are necessary through which - depthdiscussioncanbeusedforcrossdisseminationbetweendi?erentdisciplines. The Dagstuhl foundation has organized a number of workshops on Mod- ing and Integration of Sensor Based Intelligent Robot Systems. The Dagstuhl seminars take place over a full week in a beautiful setting in the Saarland in Germany. The setting provides an ideal environment for in-depth presentations and rich interaction between the participants. This volume contains papers presented during the fourth workshop held - tober 15–20, 2000. All papers were submitted by workshop attendees, and were reviewed by at least one reviewer. We wish to thank all of the reviewers for their invaluable help in making this a high-quality selection of papers. We gratefully acknowledge the support of the Schloss Dagstuhl Foundation and the sta? at Springer-Verlag. Without their support the production of this volume would not have been possible.
E-bok
PDF, Engelska, 2003710 kr
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Robotics is a highly interdisciplinary research topic, that requires integration of methods for mechanics, control engineering, signal processing, planning, gra- ics, human-computer interaction, real-time systems, applied mathematics, and software engineering to enable construction of fully operational systems. The diversity of topics needed to design, implement, and deploy such systems implies that it is almost impossible for individual teams to provide the needed critical mass for such endeavors. To facilitate interaction and progress on sensor-based intelligent robotics inter-disciplinary workshops are necessary through which - depthdiscussioncanbeusedforcrossdisseminationbetweendi?erentdisciplines. The Dagstuhl foundation has organized a number of workshops on Mod- ing and Integration of Sensor Based Intelligent Robot Systems. The Dagstuhl seminars take place over a full week in a beautiful setting in the Saarland in Germany. The setting provides an ideal environment for in-depth presentations and rich interaction between the participants. This volume contains papers presented during the fourth workshop held - tober 15–20, 2000. All papers were submitted by workshop attendees, and were reviewed by at least one reviewer. We wish to thank all of the reviewers for their invaluable help in making this a high-quality selection of papers. We gratefully acknowledge the support of the Schloss Dagstuhl Foundation and the sta? at Springer-Verlag. Without their support the production of this volume would not have been possible.
E-bok
PDF, Engelska, 2007710 kr
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Del 180 - Informatik-Fachberichte
Mustererkennung 1988
10. DAGM-Symposium, Zürich, 27.–29. September 1988. Proceedings
Häftad, Tyska, 1988
576 kr
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Der Band enthält die Vorträge, die auf dem 10. DAGM-Symposium Ende September 1988 in Zürich gehalten wurden. Die DAGM veranstaltet seit 1978 jährlich an verschiedenen Orten ein wissenschaftliches Symposium mit dem Ziel, Aufgabenstellungen, Denkweisen und Forschungsergebnisse aus verschiedenen Gebieten der Mustererkennung vorzustellen, den Erfahrungs- und Ideenaustausch zwischen den Fachleuten anzuregen und den Nachwuchs zu fördern. Die Beiträge zum Symposium kommen aus dem gesamten deutschen Sprachraum und darüber hinaus. Die DAGM ist Mitglied der International Association for Pattern Recognition (IAPR).
Inbunden, Tyska, 1996
1 312 kr
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Dieses Buch bietet eine systematische Einführung in dieses neue Arbeitsfeld der automatischen Bildanalyse. Es behandelt sämtliche wichtigen Teilaspekte, beginnend mit der Gewinnung von Tiefenbildern durch passive und aktive Verfahren über die Extraktion charakteristischer Flächenmerkmale und die Segmentierung bis hin zur modellbasierten Objekterkennung. Daneben werden konkrete Anwendungen der Tiefenbildanalyse vorgestellt. Die Didaktik des Buches erlaubt es Forschern und Praktikern, sich selbständig in dieses Gebiet einzuarbeiten. Das teilweise schwer zugängliche Material wurde in einheitlicher Notation und verständlicher Form aufbereitet. Die beschriebenen Verfahren können damit leicht auf dem Computer implementiert werden. Die Literaturhinweise geben einen vollständigen Überblick über die aktuelle Forschung.
Häftad, Engelska, 1999
544 kr
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Robotics is a highly interdisciplinary research topic, that requires integ- tion of methods for mechanics, control engineering, signal processing, planning, graphics, human-computer interaction, real-time systems, applied mathematics, and software engineering to enable construction of fully operational systems. The diversity of topics needed to design, implement, and deploy such systems implies that it is almost impossible for individual teams to provide the critical mass required for such endeavours.To facilitate interaction and progresson s- sor based intelligent robotics the organisation of inter-disciplinary workshops is necessarythrough which in-depth discussion can be used for cross dissemination between di?erent disciplines. The Dagstuhlfoundation has organiseda number of workshopsonModelling and Integration of Sensor Based Intelligent Robot Systems. The Dagstuhl s- inars are all organised over a full week in a beautiful setting in the Saarland in Germany. The setting provides an ideal environment for in-depth presen- tions and rich interactions between the participants.This volume contains the papers presented during the third workshopheld over the period September 28 - October 2, 1998. All papers have been reviewed by one-three reviewers over a relativly short period. We wish to thank all the reviewers for their invaluable help in making this a high quality selection of papers. We gratefully acknowledge the support of the Schloss Dagstuhl Foundation and the sta? at Springer-Verlag. Without their support the production of this volume would not have been possible.
Inbunden, Engelska, 2007
1 081 kr
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Graph theory has strong historical roots in mathematics, especially in topology. Its birth is usually associated with the “four-color problem” posed by Francis Guthrie 1 in 1852, but its real origin probably goes back to the Seven Bridges of Konigsber ¨ g 2 problem proved by Leonhard Euler in 1736. A computational solution to these two completely different problems could be found after each problem was abstracted to the level of a graph model while ignoring such irrelevant details as country shapes or cross-river distances. In general, a graph is a nonempty set of points (vertices) and the most basic information preserved by any graph structure refers to adjacency relationships (edges) between some pairs of points. In the simplest graphs, edges do not have to hold any attributes, except their endpoints, but in more sophisticated graph structures, edges can be associated with a direction or assigned a label. Graph vertices can be labeled as well. A graph can be represented graphically as a drawing (vertex=dot,edge=arc),but,aslongaseverypairofadjacentpointsstaysconnected by the same edge, the graph vertices can be moved around on a drawing without changing the underlying graph structure. The expressive power of the graph models placing a special emphasis on c- nectivity between objects has made them the models of choice in chemistry, physics, biology, and other ?elds.
E-bok
PDF, Engelska, 20071 413 kr
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Graph theory has strong historical roots in mathematics, especially in topology. Its birth is usually associated with the “four-color problem” posed by Francis Guthrie 1 in 1852, but its real origin probably goes back to the Seven Bridges of Konigsber ¨ g 2 problem proved by Leonhard Euler in 1736. A computational solution to these two completely different problems could be found after each problem was abstracted to the level of a graph model while ignoring such irrelevant details as country shapes or cross-river distances. In general, a graph is a nonempty set of points (vertices) and the most basic information preserved by any graph structure refers to adjacency relationships (edges) between some pairs of points. In the simplest graphs, edges do not have to hold any attributes, except their endpoints, but in more sophisticated graph structures, edges can be associated with a direction or assigned a label. Graph vertices can be labeled as well. A graph can be represented graphically as a drawing (vertex=dot,edge=arc),but,aslongaseverypairofadjacentpointsstaysconnected by the same edge, the graph vertices can be moved around on a drawing without changing the underlying graph structure. The expressive power of the graph models placing a special emphasis on c- nectivity between objects has made them the models of choice in chemistry, physics, biology, and other ?elds.
Del 91 - Studies in Computational Intelligence
Applied Pattern Recognition
Inbunden, Engelska, 2008
1 666 kr
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A sharp increase in the computing power of modern computers, accompanied by a decrease in the data storage costs, has triggered the development of extremely powerful algorithms that can analyze complex patterns in large amounts of data within a very short period of time. Consequently, it has become possible to apply pattern recognition techniques to new tasks characterized by tight real-time requirements (e.g., person identification) and/or high complexity of raw data (e.g., clustering trajectories of mobile objects). The main goal of this book is to cover some of the latest application domains of pattern recognition while presenting novel techniques that have been developed or customized in those domains.
E-bok
PDF, Engelska, 20082 044 kr
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A sharp increase in the computing power of modern computers, accompanied by a decrease in the data storage costs, has triggered the development of extremely powerful algorithms that can analyze complex patterns in large amounts of data within a very short period of time. Consequently, it has become possible to apply pattern recognition techniques to new tasks characterized by tight real-time requirements (e.g., person identification) and/or high complexity of raw data (e.g., clustering trajectories of mobile objects). The main goal of this book is to cover some of the latest application domains of pattern recognition while presenting novel techniques that have been developed or customized in those domains.
Del 52 - Studies in Computational Intelligence
Applied Graph Theory in Computer Vision and Pattern Recognition
Häftad, Engelska, 2010
1 081 kr
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Graph theory has strong historical roots in mathematics, especially in topology. Its birth is usually associated with the “four-color problem” posed by Francis Guthrie 1 in 1852, but its real origin probably goes back to the Seven Bridges of Konigsber ¨ g 2 problem proved by Leonhard Euler in 1736. A computational solution to these two completely different problems could be found after each problem was abstracted to the level of a graph model while ignoring such irrelevant details as country shapes or cross-river distances. In general, a graph is a nonempty set of points (vertices) and the most basic information preserved by any graph structure refers to adjacency relationships (edges) between some pairs of points. In the simplest graphs, edges do not have to hold any attributes, except their endpoints, but in more sophisticated graph structures, edges can be associated with a direction or assigned a label. Graph vertices can be labeled as well. A graph can be represented graphically as a drawing (vertex=dot,edge=arc),but,aslongaseverypairofadjacentpointsstaysconnected by the same edge, the graph vertices can be moved around on a drawing without changing the underlying graph structure. The expressive power of the graph models placing a special emphasis on c- nectivity between objects has made them the models of choice in chemistry, physics, biology, and other ?elds.
Del 91 - Studies in Computational Intelligence
Applied Pattern Recognition
Häftad, Engelska, 2010
1 618 kr
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A sharp increase in the computing power of modern computers, accompanied by a decrease in the data storage costs, has triggered the development of extremely powerful algorithms that can analyze complex patterns in large amounts of data within a very short period of time. Consequently, it has become possible to apply pattern recognition techniques to new tasks characterized by tight real-time requirements (e.g., person identification) and/or high complexity of raw data (e.g., clustering trajectories of mobile objects). The main goal of this book is to cover some of the latest application domains of pattern recognition while presenting novel techniques that have been developed or customized in those domains.
E-bok
PDF, Tyska, 20131 240 kr
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Dieses Buch bietet eine systematische Einführung in dieses neue Arbeitsfeld der automatischen Bildanalyse. Es behandelt sämtliche wichtigen Teilaspekte, beginnend mit der Gewinnung von Tiefenbildern durch passive und aktive Verfahren über die Extraktion charakteristischer Flächenmerkmale und die Segmentierung bis hin zur modellbasierten Objekterkennung. Daneben werden konkrete Anwendungen der Tiefenbildanalyse vorgestellt. Die Didaktik des Buches erlaubt es Forschern und Praktikern, sich selbständig in dieses Gebiet einzuarbeiten. Das teilweise schwer zugängliche Material wurde in einheitlicher Notation und verständlicher Form aufbereitet. Die beschriebenen Verfahren können damit leicht auf dem Computer implementiert werden. Die Literaturhinweise geben einen vollständigen Überblick über die aktuelle Forschung.
Häftad, Tyska, 2011
910 kr
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Dieses Buch bietet eine systematische Einführung in dieses neue Arbeitsfeld der automatischen Bildanalyse. Es behandelt sämtliche wichtigen Teilaspekte, beginnend mit der Gewinnung von Tiefenbildern durch passive und aktive Verfahren über die Extraktion charakteristischer Flächenmerkmale und die Segmentierung bis hin zur modellbasierten Objekterkennung. Daneben werden konkrete Anwendungen der Tiefenbildanalyse vorgestellt. Die Didaktik des Buches erlaubt es Forschern und Praktikern, sich selbständig in dieses Gebiet einzuarbeiten. Das teilweise schwer zugängliche Material wurde in einheitlicher Notation und verständlicher Form aufbereitet. Die beschriebenen Verfahren können damit leicht auf dem Computer implementiert werden. Die Literaturhinweise geben einen vollständigen Überblick über die aktuelle Forschung.
E-bok
PDF, Engelska, 2012732 kr
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Document image analysis is the automatic computerinterpretation of images of printed and handwrittendocuments, including text, drawings, maps, music scores,etc. Research in this field supports a rapidly growinginternational industry. This is the first book to offer abroad selection of state-of-the-art research papers,including authoritative critical surveys of the literature,and parallel studies of the architectureof completehigh-performance printed-document reading systems. A uniquefeature is the extended section on music notation, an idealvehicle for international sharing of basic research. Also,the collection includes important new work on line drawings,handwriting, character and symbol recognition, and basicmethodological issues. The IAPR 1990 Workshop on Syntacticand Structural Pattern Recognition is summarized,includingthe reports of its expert working groups, whose debatesprovide a fascinating perspective on the field.The book is an excellent text for a first-year graduateseminar in document image analysis,and is likely to remaina standard reference in the field for years.
Häftad, Engelska, 2011
560 kr
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Document image analysis is the automatic computerinterpretation of images of printed and handwrittendocuments, including text, drawings, maps, music scores,etc. Research in this field supports a rapidly growinginternational industry. This is the first book to offer abroad selection of state-of-the-art research papers,including authoritative critical surveys of the literature,and parallel studies of the architectureof completehigh-performance printed-document reading systems. A uniquefeature is the extended section on music notation, an idealvehicle for international sharing of basic research. Also,the collection includes important new work on line drawings,handwriting, character and symbol recognition, and basicmethodological issues. The IAPR 1990 Workshop on Syntacticand Structural Pattern Recognition is summarized,includingthe reports of its expert working groups, whose debatesprovide a fascinating perspective on the field.The book is an excellent text for a first-year graduateseminar in document image analysis,and is likely to remaina standard reference in the field for years.
E-bok
PDF, Engelska, 2012710 kr
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Thirty years ago pattern recognition was dominated by the learning machine concept: that one could automate the process of going from the raw data to a classifier. The derivation of numerical features from the input image was not considered an important step. One could present all possible features to a program which in turn could find which ones would be useful for pattern recognition. In spite of significant improvements in statistical inference techniques, progress was slow. It became clear that feature derivation was a very complex process that could not be automated and that features could be symbolic as well as numerical. Furthennore the spatial relationship amongst features might be important. It appeared that pattern recognition might resemble language analysis since features could play the role of symbols strung together to form a word. This led. to the genesis of syntactic pattern recognition, pioneered in the middle and late 1960''s by Russel Kirsch, Robert Ledley, Nararimhan, and Allan Shaw. However the thorough investigation of the area was left to King-Sun Fu and his students who, until his untimely death, produced most of the significant papers in this area. One of these papers (syntactic recognition of fingerprints) received the distinction of being selected as the best paper published that year in the IEEE Transaction on Computers. Therefore syntactic pattern recognition has a long history of active research and has been used in industrial applications.
Del 45 - NATO ASI Subseries F:
Syntactic and Structural Pattern Recognition
Häftad, Engelska, 2011
544 kr
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Thirty years ago pattern recognition was dominated by the learning machine concept: that one could automate the process of going from the raw data to a classifier. The derivation of numerical features from the input image was not considered an important step. One could present all possible features to a program which in turn could find which ones would be useful for pattern recognition. In spite of significant improvements in statistical inference techniques, progress was slow. It became clear that feature derivation was a very complex process that could not be automated and that features could be symbolic as well as numerical. Furthennore the spatial relationship amongst features might be important. It appeared that pattern recognition might resemble language analysis since features could play the role of symbols strung together to form a word. This led. to the genesis of syntactic pattern recognition, pioneered in the middle and late 1960's by Russel Kirsch, Robert Ledley, Nararimhan, and Allan Shaw. However the thorough investigation of the area was left to King-Sun Fu and his students who, until his untimely death, produced most of the significant papers in this area. One of these papers (syntactic recognition of fingerprints) received the distinction of being selected as the best paper published that year in the IEEE Transaction on Computers. Therefore syntactic pattern recognition has a long history of active research and has been used in industrial applications.
E-bok
PDF, Tyska, 2013585 kr
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Der Band enthält die Vorträge, die auf dem 10. DAGM-Symposium Ende September 1988 in Zürich gehalten wurden. Die DAGM veranstaltet seit 1978 jährlich an verschiedenen Orten ein wissenschaftliches Symposium mit dem Ziel, Aufgabenstellungen, Denkweisen und Forschungsergebnisse aus verschiedenen Gebieten der Mustererkennung vorzustellen, den Erfahrungs- und Ideenaustausch zwischen den Fachleuten anzuregen und den Nachwuchs zu fördern. Die Beiträge zum Symposium kommen aus dem gesamten deutschen Sprachraum und darüber hinaus. Die DAGM ist Mitglied der International Association for Pattern Recognition (IAPR).
Del 68 - Studies in Fuzziness and Soft Computing
Data Mining and Computational Intelligence
Inbunden, Engelska, 2001
1 666 kr
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The volume offers a comprehensive coverage of the recent advances in the application of soft computing and fuzzy logic theory to data mining and knowledge discovery databases. It focuses on some of the hardest, and yet unsolved, issues of data mining like understandability of patterns, finding complex relationships between attributes, handling missing and noisy data, mining very large datasets, change detection in time series, and integration of the discovery process with database management systems.
E-bok
PDF, Engelska, 20132 105 kr
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Many business decisions are made in the absence of complete information about the decision consequences. Credit lines are approved without knowing the future behavior of the customers; stocks are bought and sold without knowing their future prices; parts are manufactured without knowing all the factors affecting their final quality; etc. All these cases can be categorized as decision making under uncertainty. Decision makers (human or automated) can handle uncertainty in different ways. Deferring the decision due to the lack of sufficient information may not be an option, especially in real-time systems. Sometimes expert rules, based on experience and intuition, are used. Decision tree is a popular form of representing a set of mutually exclusive rules. An example of a two-branch tree is: if a credit applicant is a student, approve; otherwise, decline. Expert rules are usually based on some hidden assumptions, which are trying to predict the decision consequences. A hidden assumption of the last rule set is: a student will be a profitable customer. Since the direct predictions of the future may not be accurate, a decision maker can consider using some information from the past. The idea is to utilize the potential similarity between the patterns of the past (e.g., "most students used to be profitable") and the patterns of the future (e.g., "students will be profitable").
Del 68 - Studies in Fuzziness and Soft Computing
Data Mining and Computational Intelligence
Häftad, Engelska, 2010
1 666 kr
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Many business decisions are made in the absence of complete information about the decision consequences. Credit lines are approved without knowing the future behavior of the customers; stocks are bought and sold without knowing their future prices; parts are manufactured without knowing all the factors affecting their final quality; etc. All these cases can be categorized as decision making under uncertainty. Decision makers (human or automated) can handle uncertainty in different ways. Deferring the decision due to the lack of sufficient information may not be an option, especially in real-time systems. Sometimes expert rules, based on experience and intuition, are used. Decision tree is a popular form of representing a set of mutually exclusive rules. An example of a two-branch tree is: if a credit applicant is a student, approve; otherwise, decline. Expert rules are usually based on some hidden assumptions, which are trying to predict the decision consequences. A hidden assumption of the last rule set is: a student will be a profitable customer. Since the direct predictions of the future may not be accurate, a decision maker can consider using some information from the past. The idea is to utilize the potential similarity between the patterns of the past (e.g., "most students used to be profitable") and the patterns of the future (e.g., "students will be profitable").