A Confidence-Guided Technique for Tracking Time-Varying Features

Journal Article · Mon Mar 01 00:00:00 EST 2021 · Computing in Science and Engineering

DOI:https://doi.org/10.1109/mcse.2020.3047979· OSTI ID:1774462

Dutta, Soumya ^[1]; Turton, Terece L. ^[1]; Ahrens, James Paul ^[1]

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1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Application scientists often employ feature tracking algorithms to capture the temporal evolution of various features in their simulation data. However, as the complexity of the scientific features is increasing with the advanced simulation modeling techniques, quantification of reliability of the feature tracking algorithms is becoming important. One of the desired requirements for any robust feature tracking algorithm is to estimate its confidence during each tracking step so that the results obtained can be interpreted without any ambiguity. To address this, we develop a confidence-guided feature tracking algorithm that allows reliable tracking of user-selected features and presents the tracking dynamics using a graph-based visualization along with the spatial visualization of the tracked feature. Here, the efficacy of the proposed method is demonstrated by applying it to two scientific datasets containing different types of time-varying features.

• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: 89233218CNA000001
• OSTI ID: 1774462
• Report Number(s): LA-UR--20-28907
• Journal Information: Computing in Science and Engineering, Journal Name: Computing in Science and Engineering Journal Issue: 2 Vol. 23; ISSN 1521-9615
• Publisher: IEEE Computer Society
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
Feature tracking
Fuzzy Logic system
Machine learning
Time-varying data
Uncertainty quantification
Visualization