Lauro Snidaro, Jesús García, James Llinas, Erik Blasch's Context-Enhanced Information Fusion: Boosting Real-World PDF

By Lauro Snidaro, Jesús García, James Llinas, Erik Blasch

ISBN-10: 3319289691

ISBN-13: 9783319289694

ISBN-10: 3319289713

ISBN-13: 9783319289717

This textual content experiences the basic concept and newest tools for together with contextual details in fusion strategy layout and implementation. Chapters are contributed through the major foreign specialists, spanning quite a few advancements and purposes. The publication highlights excessive- and low-level details fusion difficulties, functionality review below hugely hard stipulations, and layout rules. a selected concentration is put on techniques that combine examine from diverse groups, emphasizing the advantage of combining various innovations to beat the constraints of a unmarried standpoint. positive factors: introduces the terminology and middle components in info fusion and context; offers key subject matters for context-enhanced details fusion; discusses layout matters in constructing context-aware fusion platforms; offers mathematical grounds for modeling the contextual affects in consultant fusion difficulties; describes the fusion of challenging and smooth facts; experiences a various variety of applications.

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This article studies the basic concept and most up-to-date tools for together with contextual info in fusion technique layout and implementation. Chapters are contributed by means of the most important foreign specialists, spanning various advancements and purposes. The publication highlights excessive- and low-level details fusion difficulties, functionality review lower than hugely difficult stipulations, and layout rules.

Extra resources for Context-Enhanced Information Fusion: Boosting Real-World Performance with Domain Knowledge

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W. Liu, J. Wei, M. Liang, Y. Cao, I. Hwang, Multi-sensor fusion and fault detection using hybrid estimation for air traffic surveillance. Aerosp. Electron. Syst. IEEE Trans. 49(4), 2323– 2339 (2013) 62. A. Benavoli, L. Chisci, A. Farina, L. Timmoneri, G. Zappa, Knowledge-based system for multi-target tracking in a littoral environment. IEEE Trans. Aerosp. Electron. Syst. 42(3), 1100–1119 (2006) 63. B. Song, Y. Abu-Mostafa, J. Sill, H. Kasdan, M. Pavel, Robust image recognition by fusion of contextual information.

As well as in various forms of intelligence reporting. Natural language sources may provide essential contextual information not available from structured sensor data alone. Natural language understanding is also vital in modeling interpersonal communications. However, natural language is itself fraught with ambiguities—phonetic, lexical, syntactic, semantic, pragmatic—that can only be resolved by contextual considerations. Context is represented by contextual information that may be obtained from various sources and formalized in different ways.

Klein, Sensor and Data Fusion: A Tool for Information Assessment and Decision Making, 2nd edn. (SPIE Press, 2012) 11. E. L. Hall, J. ), Handbook of Multisensor Data Fusion: Theory and Practice, 2nd edn. The Electrical Engineering and Applied Signal Processing Series (CRC Press, Boca Raton, FL, 2008) 1 Context and Fusion: Definitions, Terminology 19 12. B. Mitchell, Multi-Sensor Data Fusion: An Introduction (Springer Publishing Company Incorporated, Heidelberg, 2007) 13. M. Abidi, R. Gonzalez, Data Fusion in Robotics and Machine Intelligence (Academic Press, San Diego, 1992) 14.

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Context-Enhanced Information Fusion: Boosting Real-World Performance with Domain Knowledge by Lauro Snidaro, Jesús García, James Llinas, Erik Blasch


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