Tracking scalar features in unstructured datasets

Deborah Silver, Xin Wang

Research output: Contribution to conferencePaperpeer-review

59 Scopus citations


3D time-varying unstructured and structured datasets are difficult to visualize and analyze because of the immense amount of data involved. These datasets contain many evolving amorphous regions, and standard visualization techniques provide no facilities to aid the scientist to follow regions of interest. In this paper, we present a basic framework for the visualization of time-varying datasets, and a new algorithm and data structure to track volume features in unstructured scalar datasets. The algorithm and data structure are general and can be used for structured, curvilinear, adaptive and hybrid grids as well. The features tracked can be any type of connected regions. Examples are shown from ongoing research.

Original languageAmerican English
Number of pages8
StatePublished - 1998
EventProceedings of the 1998 IEEE Visualization Conference - Research Triangle Park, NC, USA
Duration: Oct 18 1998Oct 23 1998


OtherProceedings of the 1998 IEEE Visualization Conference
CityResearch Triangle Park, NC, USA

ASJC Scopus subject areas

  • Software
  • Computer Science(all)
  • Engineering(all)
  • Computer Graphics and Computer-Aided Design


Dive into the research topics of 'Tracking scalar features in unstructured datasets'. Together they form a unique fingerprint.

Cite this