Research Highlights

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... Read more

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