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Fast Topology-based Feature Tracking using a Directed Acyclic Graph
KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
(English)Manuscript (preprint) (Other academic)
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-213965OAI: oai:DiVA.org:kth-213965DiVA: diva2:1139362
Note

QC 20171020

Available from: 2017-09-07 Created: 2017-09-07 Last updated: 2018-01-13Bibliographically approved
In thesis
1. Comparison and Tracking Methods for Interactive Visualization of Topological Structures in Scalar Fields
Open this publication in new window or tab >>Comparison and Tracking Methods for Interactive Visualization of Topological Structures in Scalar Fields
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Scalar fields occur quite commonly in several application areas in both static and time-dependent forms. Hence a proper visualization of scalar fieldsneeds to be equipped with tools to extract and focus on important features of the data. Similarity detection and pattern search techniques in scalar fields present a useful way of visualizing important features in the data. This is done by isolating these features and visualizing them independently or show all similar patterns that arise from a given search pattern. Topological features are ideal for this purpose of isolating meaningful patterns in the data set and creating intuitive feature descriptors. The Merge Tree is one such topological feature which has characteristics ideally suited for this purpose. Subtrees of merge trees segment the data into hierarchical regions which are topologically defined. This kind of feature-based segmentation is more intelligent than pure data based segmentations involving windows or bounding volumes. In this thesis, we explore several different techniques using subtrees of merge trees as features in scalar field data. Firstly, we begin with a discussion on static scalar fields and devise techniques to compare features - topologically segmented regions given by the subtrees of the merge tree - against each other. Second, we delve into time-dependent scalar fields and extend the idea of feature comparison to spatio-temporal features. In this process, we also come up with a novel approach to track features in time-dependent data considering the entire global network of likely feature associations between consecutive time steps.The highlight of this thesis is the interactivity that is enabled using these feature-based techniques by the real-time computation speed of our algorithms. Our techniques are implemented in an open-source visualization framework Inviwo and are published in several peer-reviewed conferences and journals.

Place, publisher, year, edition, pages
KTH Royal Institute of Technology, 2017. 55 p.
Series
TRITA-CSC-A, ISSN 1653-5723 ; 2017:23
Keyword
topology, scalar fields, merge tree, tree comparison, tracking, similarity search
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-216375 (URN)978-91-7729-580-8 (ISBN)
Public defence
2017-11-15, Visualization Studio VIC, Lindstedtsvägen 7, Stockholm, 13:00 (English)
Opponent
Supervisors
Funder
Swedish e‐Science Research Center
Note

QC 20171020

Available from: 2017-10-20 Created: 2017-10-19 Last updated: 2018-01-13Bibliographically approved

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Citation style
  • apa
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Output format
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