Feature detection and display are the essential goals of the visualization process. Most visualization software achieves these goals by mapping properties of sampled intensity values and their derivatives to color and opacity. In this work, we propose to explicitly study the local frequency distribution of intensity values in broader neighborhoods centered around each voxel. We have found frequency distributions to contain meaningful and quantitative information that is relevant for many kinds of feature queries. Our approach allows users to enter predicate-based hypotheses about relational patterns in local distributions and render visualizations that show how neighborhoods match the predicates. Distributions are a familiar concept to nonexpert users, and we have built a simple graphical user interface for forming and testing queries interactively. The query framework readily applies to arbitrary spatial data sets and supports queries on time variant and multifield data. Users can directly query for classes of features previously inaccessible in general feature detection tools. Using several well-known data sets, we show new quantitative features that enhance our understanding of familiar visualization results.

Abstract In this work we present 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.

We propose a distributed data management scheme for large data visualization that emphasizes efficient data sharing and access. To minimize data access time and support users with a variety of local computing capabilities, we introduce an adaptive data selection method based on an "enhanced time-space partitioning" (ETSP) tree that assists with effective visibility culling, as well as multiresolution data selection. By traversing the tree, our data management algorithm can quickly identify the visible regions of data, and, for each region, adaptively choose the lowest resolution satisfying user-specified error tolerances. Only necessary data elements are accessed and sent to the visualization pipeline. To further address the issue of sharing large-scale data among geographically distributed collaborative teams, we have designed an infrastructure for integrating our data management technique with a distributed data storage system provided by logistical networking (LoN). Data sets at different resolutions are generated and uploaded to LoN for wide-area access. We describe a parallel volume rendering system that verifies the effectiveness of our data storage, selection and access scheme.