seemulti - Concurrent Viewing of Multiple Attribute-Specific Subspaces
It is common for scientific visualization production tools to
provide side-by-side images showing various results of significance.
This is particularly true for applications involving
time-varying datasets with a large number of variables.
However, application scientists would often prefer to have
these results summarized into the fewest possible images. In
this work, we are interested in developing a general scientific
visualization method that addresses this issue.
We accomplish this with a point classification algorithm for multi-variate data.
Our method is based on the concept
of attribute subspaces, which are derived from a set of user specified attribute target values. Our classification
approach enables users to visually distinguish regions of saliency through concurrent viewing of these subspaces
in single images. We also allow a user to threshold the data according to a specified distance from attribute target
values. Based on the degree of thresholding, the remaining data points are assigned radii of influence that are used
for the final coloring. This limits the view to only those points that are most relevant, while maintaining a similar
visual context.
The above figure demonstrates attribute summary.
(a)-(e): Images we created using ncBrowse (http://www.epic.noaa.gov/java/ncBrowse/) from single variables of the jet combustion
dataset. (f): Image created by our method fusing high valued ranges of each of the single variable images.
'Concurrent Viewing of Multiple Attribute-Specific Subspaces', Robert Sisneros, C. Ryan Johnson and Jian Huang, Computer Graphics Forum (special issue for EuroVisŐ08), 27(3), pp. 783-790, 2008.
