A Structural Average of Labeled Merge Trees for Uncertainty Visualization.

ABSTRACT: Physical phenomena in science and engineering are frequently modeled using scalar fields. In scalar field topology, graph-based topological descriptors such as merge trees, contour trees, and Reeb graphs are commonly used to characterize topological changes in the (sub)level sets of scalar fields. One of the biggest challenges and opportunities to advance topology-based visualization is to understand and incorporate uncertainty into such topological descriptors to effectively reason about their underlying data. In this paper, we study a structural average of a set of labeled merge trees and use it to encode uncertainty in data. Specifically, we compute a 1-center tree that minimizes its maximum distance to any other tree in the set under a well-defined metric called the interleaving distan

SUBMITTER: Yan L 

PROVIDER: S-EPMC7752151 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature