Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6865787 | Neurocomputing | 2015 | 21 Pages |
Abstract
Recent trends in human-computer interaction (HCI) show a development towards cognitive technical systems (CTS) to provide natural and efficient operating principles. To do so, a CTS has to rely on data from multiple sensors which must be processed and combined by fusion algorithms. Furthermore, additional sources of knowledge have to be integrated, to put the observations made into the correct context. Research in this field often focuses on optimizing the performance of the individual algorithms, rather than reflecting the requirements of CTS. This paper presents the information fusion principles in CTS architectures we developed for Companion Technologies. Combination of information generally goes along with the level of abstractness, time granularity and robustness, such that large CTS architectures must perform fusion gradually on different levels - starting from sensor-based recognitions to highly abstract logical inferences. In our CTS application we sectioned information fusion approaches into three categories: perception-level fusion, knowledge-based fusion and application-level fusion. For each category, we introduce examples of characteristic algorithms. In addition, we provide a detailed protocol on the implementation performed in order to study the interplay of the developed algorithms.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Michael Glodek, Frank Honold, Thomas Geier, Gerald Krell, Florian Nothdurft, Stephan Reuter, Felix Schüssel, Thilo Hörnle, Klaus Dietmayer, Wolfgang Minker, Susanne Biundo, Michael Weber, Günther Palm, Friedhelm Schwenker,