Intelligent feature extraction and tracking for visualizing large-scale 4P flow simulations

Fan Yin Tzeng, Kwan-Liu Ma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

38 Scopus citations


Terascale simulations produce data that is vast in spatial, temporal, and variable domains, creating a formidable challenge for subsequent analysis. Feature extraction as a data reduction method offers a viable solution to this large data problem. This paper presents a new approach to the problem of extracting and visualizing 4D features within large volume data. Conventional methods requires either an analytical description of the feature of interest or tedious manual intervention throughout the feature extraction and tracking process. We show that it is possible for a visualization system to "learn" to extract and track features in complex 4D flow field according to their "visual" properties, location, shape, and size. The basic approach is to employ machine learning in the process of visualization. Such an intelligent system approach is powerful because it allows us to extract and track an feature of interest in a high-dimensional space without explicitly specifying the relations between those dimensions, resulting in a greatly simplified and intuitive visualization interface.

Original languageEnglish (US)
Title of host publicationProceedings - Thirteenth International Symposium on Temporal Representation and Reasoning, TIME 2006
StatePublished - Dec 1 2005
EventACM/IEEE 2005 Supercomputing Conference, SC'05 - Seatle, WA, United States
Duration: Nov 12 2005Nov 18 2005


OtherACM/IEEE 2005 Supercomputing Conference, SC'05
CountryUnited States
CitySeatle, WA


  • Artificial neural networks
  • Feature extraction
  • Feature tracking
  • Flow visualization
  • Hardware acceleration
  • Machine learning
  • Time-varying volume data
  • User interface

ASJC Scopus subject areas

  • Engineering(all)

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