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Uncertainty makes the analysis of even simple situations difficult. It forces situation analysts to formulate and manage hypotheses during the construction of the explicit representations of the real world situations. Because of human cognitive limitations, this may quickly become overwhelming, even for the most experienced and capable analysts. In an attempt to provide better support systems, this...
This paper highlights the importance of uncertainty visualization in information fusion, reviews general methods of representing uncertainty and presents perceptual and cognitive principles from Tufte, Chambers and Bertin as well as users experiments documented in the literature. Examples of uncertainty representations in information fusion are analyzed using these general theories. These principles...
This paper deals with enriched qualitative belief functions for reasoning under uncertainty and for combining information expressed in natural language through linguistic labels. In this work, two possible enrichments (quantitative and/or qualitative) of linguistic labels are considered and operators (addition, multiplication, division, etc) for dealing with them are proposed and explained. We denote...
Data fusion within the evidential reasoning framework is a well established, robust and conservative technique to fuse uncertain information from multiple sensors. A number of fusion methods within this formalism were introduced including Dempster-Shafer theory (DST) fusion, Dezert Samarandche fusion (DSmT), and Smets' transferable belief model (TBM) based fusion. However, the impact of fusion on...
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