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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.