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When was the last time you saw an isosurface with error bars or streamlines with standard deviations or volume visualizations with representations of confidence intervals? With few exceptions, visualization research has ignored the visual representation of errors and uncertainty for 3D visualizations. However, if you look at peer-reviewed science and engineering journals, you will see that the majority of 2D graphs represent error and/or uncertainty within the experimental or simulated data. Why the difference? Clearly, if it s important to represent error and uncertainty in 2D graphs, then it s equally important to represent error and uncertainty in 2D and 3D visualization.

This paper discusses the need for uncertainty visualization in the scientific imaging field. Typically error and uncertainty of data are included as 2D graphs but left out of 2 and 3D visualizations. A major reason for this is the difficulty in expressing these ideas in a visually pleasing and informative manner. Error and uncertainty can be added to a data set in a number of ways, during the acquisition, in how the data is represented geometrically or mathematically, during the translation to a visual form (i.e. re-sampling, filtering, quantization, and rescaling), and in the actual visualization. The expression of this error or uncertainty can be expressed by various techniques (depending on the application) such as glyphs which can either be new glyphs specific for conveying error, or by altering the size and orientation of current glyphs using error or uncertainty as a variable. Also adding new visual cues to the visualization has been used as a way to convey uncertainty, bump maps, blurring, textures, and other lighting attributes can be used, taking advantage of the fact that these techniques may distract the eye, thus giving a cue that there are problems in these areas. A primary goal of visualization is to convey all relevant data to the viewer, and error/uncertainty are important characteristics that should be included in a visualization.

visualization, uncertainty,cfd computational fluid dynamics, scientific computing, error visualization

Use isosurfaces to represent the average value of the scalar field and the error or uncertainty using volume rendering with opacity.

@Article{	johnson:2003:VEAU,
author =	"Chris R. Johnson and Allen R. Sanderson",
title =	"A Next Step: Visualizing Errors and Uncertainty",
journal =	"{IEEE} Computer Graphics and Applications",
volume =	"23",
number =	"5",
pages =	"6--10",
year =	"2003",
editor =	"Theresa-Marie Rhyne",
}