Trajectory-based flow feature tracking in joint particle/volume datasets

Franz Sauer, Hongfeng Yu, Kwan Liu Ma

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

Studying the dynamic evolution of time-varying volumetric data is essential in countless scientific endeavors. The ability to isolate and track features of interest allows domain scientists to better manage large complex datasets both in terms of visual understanding and computational efficiency. This work presents a new trajectory-based feature tracking technique for use in joint particle/volume datasets. While traditional feature tracking approaches generally require a high temporal resolution, this method utilizes the indexed trajectories of corresponding Lagrangian particle data to efficiently track features over large jumps in time. Such a technique is especially useful for situations where the volume dataset is either temporally sparse or too large to efficiently track a feature through all intermediate timesteps. In addition, this paper presents a few other applications of this approach, such as the ability to efficiently track the internal properties of volumetric features using variables from the particle data. We demonstrate the effectiveness of this technique using real world combustion and atmospheric datasets and compare it to existing tracking methods to justify its advantages and accuracy.

Original languageEnglish (US)
Article number6875975
Pages (from-to)2565-2574
Number of pages10
JournalIEEE Transactions on Visualization and Computer Graphics
Volume20
Issue number12
DOIs
StatePublished - Dec 31 2014

Keywords

  • Feature extraction and tracking
  • Flow visualization
  • Particle data
  • Particle trajectories
  • Volume data

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

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