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Visualization of Uncertainty without a Mean
Jan.-Feb. 2013 (vol. 33 no. 1)
pp. 75-79
As dataset size and complexity steadily increase, uncertainty is becoming an important data aspect. So, today's visualizations need to incorporate indications of uncertainty. However, characterizing uncertainty for visualization isn't always straightforward. Entropy, in the information-theoretic sense, can be a measure for uncertainty in categorical datasets. The authors discuss the mathematical formulation, interpretation, and use of entropy in visualizations. This research aims to demonstrate entropy as a metric and expand the vocabulary of uncertainty measures for visualization.
Index Terms:
Entropy,Data visualization,Uncertainty,Image color analysis,Measurement uncertainty,Magnetic resonance imaging,Visualization,color mapping,Entropy,Data visualization,Uncertainty,Image color analysis,Measurement uncertainty,Magnetic resonance imaging,Visualization,computer graphics,uncertainty,entropy,volume rendering
Citation:
K. Potter, S. Gerber, E. W. Anderson, "Visualization of Uncertainty without a Mean," IEEE Computer Graphics and Applications, vol. 33, no. 1, pp. 75-79, Jan.-Feb. 2013, doi:10.1109/MCG.2013.14
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