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Article

Intelligent Feature Extraction and Tracking for Visualizing Large-Scale 4D Flow Simulations

Authors:

Kwan-Liu MaAuthors Info & Claims

SC '05: Proceedings of the 2005 ACM/IEEE conference on Supercomputing

Page 6

https://doi.org/10.1109/SC.2005.37

Published: 12 November 2005 Publication History

Abstract

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.

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Cited By

Singh UVinayachandran PNatarajan V(2024)Advection-based tracking and analysis of salinity movement in the Indian OceanComputers & Geosciences10.1016/j.cageo.2023.105493182:COnline publication date: 4-Mar-2024
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Singh UDhipu TVinayachandran PNatarajan V(2022)Front and skeleton features based methods for tracking salinity propagation in the oceanComputers & Geosciences10.1016/j.cageo.2021.104993159:COnline publication date: 1-Feb-2022
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Index Terms

Intelligent Feature Extraction and Tracking for Visualizing Large-Scale 4D Flow Simulations

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cover image ACM Conferences

SC '05: Proceedings of the 2005 ACM/IEEE conference on Supercomputing

November 2005

829 pages

ISBN:1595930612

General Chair:
William Kramer

Sponsors

SIGARCH: ACM Special Interest Group on Computer Architecture
IEEE-CS: Computer Society

Publisher

IEEE Computer Society

United States

Publication History

Published: 12 November 2005

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SC '05

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SIGARCH
IEEE-CS

SC '05: International Conference for High Performance Computing, Networking, Storage and Analysis

November 12 - 18, 2005

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SC '05 Paper Acceptance Rate 62 of 260 submissions, 24%;

Overall Acceptance Rate 1,516 of 6,373 submissions, 24%

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Cited By

Singh UVinayachandran PNatarajan V(2024)Advection-based tracking and analysis of salinity movement in the Indian OceanComputers & Geosciences10.1016/j.cageo.2023.105493182:COnline publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1016/j.cageo.2023.105493
Singh UDhipu TVinayachandran PNatarajan V(2022)Front and skeleton features based methods for tracking salinity propagation in the oceanComputers & Geosciences10.1016/j.cageo.2021.104993159:COnline publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1016/j.cageo.2021.104993
Heinemann MFrey STkachev GStraub ASadlo FErtl T(2021)Visual analysis of droplet dynamics in large-scale multiphase spray simulationsJournal of Visualization10.1007/s12650-021-00750-624:5(943-961)Online publication date: 1-Oct-2021
https://dl.acm.org/doi/10.1007/s12650-021-00750-6
Krone MReina GSchulz CKulschewski TPleiss JErtl TScheuermann G(2013)Interactive extraction and tracking of biomolecular surface featuresProceedings of the 15th Eurographics Conference on Visualization10.1111/cgf.12120(331-340)Online publication date: 17-Jun-2013
https://dl.acm.org/doi/10.1111/cgf.12120
Crnovrsanin TLiao IWuy YMa K(2011)Visual recommendations for network navigationProceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization10.1111/j.1467-8659.2011.01957.x(1081-1090)Online publication date: 1-Jun-2011
https://dl.acm.org/doi/10.1111/j.1467-8659.2011.01957.x
Tikhonova ACorrea CMa K(2010)An exploratory technique for coherent visualization of time-varying volume dataProceedings of the 12th Eurographics / IEEE - VGTC conference on Visualization10.1111/j.1467-8659.2009.01690.x(783-792)Online publication date: 9-Jun-2010
https://dl.acm.org/doi/10.1111/j.1467-8659.2009.01690.x
Kendall WGlatter MHuang JPeterka TLatham RRoss RPinfold W(2009)Terascale data organization for discovering multivariate climatic trendsProceedings of the Conference on High Performance Computing Networking, Storage and Analysis10.1145/1654059.1654075(1-12)Online publication date: 14-Nov-2009
https://dl.acm.org/doi/10.1145/1654059.1654075
Woodring JShen H(2009)Semi-automatic time-series transfer functions via temporal clustering and sequencingProceedings of the 11th Eurographics / IEEE - VGTC conference on Visualization10.1111/j.1467-8659.2009.01472.x(791-798)Online publication date: 10-Jun-2009
https://dl.acm.org/doi/10.1111/j.1467-8659.2009.01472.x
Mallinson G(2008)CFD visualisationInternational Journal of Computational Fluid Dynamics10.1080/1061856070174308622:1-2(49-59)Online publication date: 1-Jan-2008
https://dl.acm.org/doi/10.1080/10618560701743086
Akibay HMay K(2007)A tri-space visualization interface for analyzing time-varying multivariate volume dataProceedings of the 9th Joint Eurographics / IEEE VGTC conference on Visualization10.5555/2384179.2384198(115-122)Online publication date: 23-May-2007
https://dl.acm.org/doi/10.5555/2384179.2384198
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