Context-enhanced information fusion [electronic resource] : boosting real-world performance with domain knowledge / Lauro Snidaro, Jesús García, James Llinas, Erik Blasch, editors.

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Context-enhanced information fusion [electronic resource] : boosting real-world performance with domain knowledge / Lauro Snidaro, Jesús García, James Llinas, Erik Blasch, editors.

9783319289717 (electronic book)
3319289713 (electronic book)
9783319289694
3319289691

Switzerland : Springer, 2016.

English

1 online resource (xviii, 703 pages) : illustrations.

10.1007/978-3-319-28971-7 doi

QA76.5915

004

This interdisciplinary text/reference 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 holistic approaches that integrate research from different communities, emphasizing the benefit of combining different techniques to overcome the limitations of a single perspective or approach. Topics and features: · Introduces the essential terminology and core elements in information fusion and context, conveyed with the support of the JDL/DFIG data fusion model · Presents key themes for context-enhanced information fusion, including topics derived from target tracking, decision support and threat assessment · Discusses design issues in developing context-aware fusion systems, proposing several architectures optimized for context access and discovery · Provides mathematical grounds for modeling the contextual influences in representative fusion problems, such as sensor quality assessment, target tracking, robotics, and text analysis · Describes the fusion of device-generated (hard) data with human-generated (soft) data · Reviews a diverse range of applications where the exploitation of contextual information in the fusion process boosts system performance This authoritative volume will be of great use to researchers, academics, and practitioners pursuing applications where information fusion offers a solution. The broad coverage will appeal to those involved in a variety of disciplines, from machine learning and data mining, to machine vision, decision support systems, and systems engineering. 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 (www.isif.org).

Includes index.

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Online resource; title from PDF title page (SpringerLink, viewed June 6, 2016).

Advances in computer vision and pattern recognition.

Print version: 9783319289694

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