Abstract

Rapid increase in the performance of computing and measurement environments as well as in opportunities to use the global network infrastructure has given rise to so-called “data big-bang”. It has motivated us to develop a cooperative visulization environment which allows the users to deal with a huge amount of data with the aid of data-centric, semi-autormatic parameter tweaking. In the past decade, we have consistently built on differential topology to come up with a set of tools for examining the global structure and local features of a target 4D volume effectively. The topological skeleton of a given snapshot volume can be represented by Volume Skeleton Tree (VST), which is a kind of level-set graph, consisting of volumetric critical points and their global connectivity to delineate the topological evolution, such as appearance, disappearance, splitting, and merging, of an isosurface according to the scalar field value. We have developed an efficient and robust method for constructing proper VSTs even from a complex and/or noisy volume using adaptive tetrahedralization and topological skeleton simplification. Our current tools to explore volumes visually with reference to the extracted VSTs include critical point-based colormap/transfer function generators; critical/representative isosurface and cross-section extractors; optimal viewpoint/light sournce locator; topologically-accentuated multi-dimensional transfer function designer; interval volume decomposer; and T-Map (critical timing finder and selective data migration). In this talk, the effectiveness of these cooperative volume exoloration tools will be illustrated with applications to real datasets from many disciplines.

Short Biography