Abraham Kandel

Horst Bunke

Mark Last

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.

Applied Graph Theory in Computer Vision and Pattern Recognition

Rolf Klein

Henrik I. Christensen

Gregory D. Hager

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.

International Workshop, Dagstuhl Castle, Germany, October 15-20, 2000. Selected Revised Papers

Sensor Based Intelligent Robots

Xiaoyi Jiang

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.

Gewinnung und Analyse von Tiefenbildern

Dreidimensionales Computersehen

Patrick Shen-pei Wang

Henry S. Baird

Interest in the automatic processing and analysis of document images has been rapidly increasing during the past few years. This book addresses the different subfields of document image analysis, including preprocessing and segmentation, form processing, handwriting recognition, line drawing and map processing, and contextual processing.

Document Image Analysis

R. C. Bolles

Hartmut Noltemeier

Rapid advances in sensors, computers, and algorithms continue to fuel dramatic improvements in intelligent robots. In addition, robot vehicles are starting to appear in a number of applications. For example, they have been installed in public settings to perform such tasks as delivering items in hospitals and cleaning floors in supermarkets; recently, two small robot vehicles were launched to explore Mars.This book presents the latest advances in the principal fields that contribute to robotics. It contains contributions written by leading experts addressing topics such as Path and Motion Planning, Navigation and Sensing, Vision and Object Recognition, Environment Modeling, and others.

Sensing, Modeling, and Planning

Intelligent Robots

Adding the time dimension to real-world databases produces Time Series Databases (TSDB) and introduces new aspects and difficulties to data mining and knowledge discovery. This book covers the state-of-the-art methodology for mining time series databases. The novel data mining methods presented in the book include techniques for efficient segmentation, indexing, and classification of noisy and dynamic time series. A graph-based method for anomaly detection in time series is described and the book also studies the implications of a novel and potentially useful representation of time series as strings. The problem of detecting changes in data mining models that are induced from temporal databases is additionally discussed. Contents: A Survey of Recent Methods for Efficient Retrieval of Similar Time Sequences (H M Lie); Indexing of Compressed Time Series (E Fink & K Pratt); Boosting Interval-Based Literal: Variable Length and Early Classification (J J Rodriguez Diez); Segmenting Time Series: A Survey and Novel Approach (E Keogh et al.); Indexing Similar Time Series under Conditions of Noise (M Vlachos et al.); Classification of Events in Time Series of Graphs (H Bunke & M Kraetzl); Median Strings--A Review (X Jiang et al.); Change Detection in Classfication Models of Data Mining (G Zeira et al.). Readership: Graduate students, reseachers and practitioners in the fields of data mining, machine learning, databases and statistics.

Data Mining in Time Series Databases

Sebastiano Impedovo

The widespread use of bankchecks in daily life makes the development of check-reading systems of fundamental relevance to banks and other financial institutions. This will improve productivity and allow advanced customer services. Therefore, many industrial companies and academic research laboratories have recently been attracted to this field, which involves several aspects, like image acquisition and preprocessing, layout analysis, preprinted data identification and recognition, user-entered data extraction, recognition of handwritten characters and words, and signature verification.The contributions collected in this book present the state of the art in the field of complete systems for bankcheck recognition, and explore the most promising trends in key aspects of this research field.

Automatic Bankcheck Processing

The field of pattern recognition has seen enormous progress since its beginnings almost 50 years ago. A large number of different approaches have been proposed. Hybrid methods aim at combining the advantages of different paradigms within a single system. Hybrid Methods in Pattern Recognition is a collection of articles describing recent progress in this emerging field. It covers topics such as the combination of neural nets with fuzzy systems or hidden Markov models, neural networks for the processing of symbolic data structures, hybrid methods in data mining, the combination of symbolic and subsymbolic learning, and so on. Also included is recent work on multiple classifier systems. Furthermore, the book deals with applications in on-line and off-line handwriting recognition, remotely sensed image interpretation, fingerprint identification, and automatic text categorization.

Hybrid Methods in Pattern Recognition

Kazuhiko Yamamoto

Document image analysis is the automatic computer interpretation of images of printed and handwritten documents, including text, drawings, maps, music scores, etc. Research in this field supports a rapidly growing international industry. This is the first book to offer a broad selection of state-of-the-art research papers, including authoritative critical surveys of the literature, and parallel studies of the architectureof complete high-performance printed-document reading systems. A unique feature is the extended section on music notation, an ideal vehicle for international sharing of basic research. Also, the collection includes important new work on line drawings, handwriting, character and symbol recognition, and basic methodological issues. The IAPR 1990 Workshop on Syntactic and Structural Pattern Recognition is summarized,including the reports of its expert working groups, whose debates provide a fascinating perspective on the field. The book is an excellent text for a first-year graduate seminar in document image analysis,and is likely to remain a standard reference in the field for years.

Structured Document Image Analysis

Marcus Liwicki

This book addresses the task of processing online handwritten notes acquired from an electronic whiteboard, which is a new modality in handwriting recognition research. The main motivation of this book is smart meeting rooms, aim to automate standard tasks usually performed by humans in a meeting. The book can be summarized as follows. A new online handwritten database is compiled, and four handwriting recognition systems are developed. Moreover, novel preprocessing and normalization strategies are designed especially for whiteboard notes and a new neural network based recognizer is applied. Commercial recognition systems are included in a multiple classifier system. The experimental results on the test set show a highly significant improvement of the recognition performance to more than 86%.

Online, Offline and Combination

Recognition of Whiteboard Notes

A sharp increase in the computing power of modern computers has triggered the development of powerful algorithms that can analyze complex patterns in large amounts of data within a short time period. Consequently, it has become possible to apply pattern recognition techniques to new tasks. 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.

Horst Bunke

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