Overview
- Provides a special focus on practical approaches to solving
- real-world problems
- Describes the application of signal and intelligence
- processing approaches to open challenges in information fusion
- Presents an holistic approach, integrating research results from different communities
- Reviews contemporary developments on fresh and challenging topics in information fusion
- Includes supplementary material: sn.pub/extras
Part of the book series: Advances in Computer Vision and Pattern Recognition (ACVPR)
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About this book
This text reviews the fundamental theory and latest methods for including contextual information in fusion process design and implementation. Chapters are contributed by the foremost international experts, spanning numerous developments and applications. The book highlights high- and low-level information fusion problems, performance evaluation under highly demanding conditions, and design principles. A particular focus is placed on approaches that integrate research from different communities, emphasizing the benefit of combining different techniques to overcome the limitations of a single perspective. Features: introduces the terminology and core elements in information fusion and context; presents key themes for context-enhanced information fusion; discusses design issues in developing context-aware fusion systems; provides mathematical grounds for modeling the contextual influences in representative fusion problems; describes the fusion of hard and soft data; reviews a diverse range of applications.
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Keywords
Table of contents (26 chapters)
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Front Matter
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Foundations
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Front Matter
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Systems Philosophy of Contextual Fusion
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Front Matter
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Mathematical Characterization of Context
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Front Matter
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Editors and Affiliations
About the editors
Dr. Lauro Snidaro is an Assistant Professor in the Department of Mathematics and Computer Science at the University of Udine, Italy. Dr. Jesús García is an Associate Professor in the Computer Science and Engineering Department at the Carlos III University of Madrid, Spain. Dr. James Llinas is an Emeritus Professor in the Department of Industrial and Systems Engineering, and in the Department of Electrical Engineering, at the State University of New York at Buffalo, NY, USA. Dr. Erik Blasch is a Principal Scientist at the Air Force Research Laboratory Information Directorate (AFRL/RIEA) in Rome, NY, USA. The editors and contributors have all been leading experts within the international society of information fusion (ISIF).
Bibliographic Information
Book Title: Context-Enhanced Information Fusion
Book Subtitle: Boosting Real-World Performance with Domain Knowledge
Editors: Lauro Snidaro, Jesús García, James Llinas, Erik Blasch
Series Title: Advances in Computer Vision and Pattern Recognition
DOI: https://doi.org/10.1007/978-3-319-28971-7
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer International Publishing Switzerland (outside the USA) 2016
Hardcover ISBN: 978-3-319-28969-4Published: 06 June 2016
Softcover ISBN: 978-3-319-80464-4Published: 30 May 2018
eBook ISBN: 978-3-319-28971-7Published: 25 May 2016
Series ISSN: 2191-6586
Series E-ISSN: 2191-6594
Edition Number: 1
Number of Pages: XVIII, 703
Number of Illustrations: 13 b/w illustrations, 229 illustrations in colour
Topics: Pattern Recognition, Information Systems Applications (incl. Internet), Artificial Intelligence, Simulation and Modeling