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Global Feature Tracking and Similarity Estimation in Time-Dependent Scalar Fields
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).ORCID iD: 0000-0002-1498-9062
2017 (English)In: Computer graphics forum (Print), ISSN 0167-7055, E-ISSN 1467-8659, Vol. 36, no 3, p. 1-11Article in journal (Refereed) Published
Abstract [en]

We present an algorithm for tracking regions in time-dependent scalar fields that uses global knowledge from all time steps for determining the tracks. The regions are defined using merge trees, thereby representing a hierarchical segmentation of the data in each time step. The similarity of regions of two consecutive time steps is measured using their volumetric overlap and a histogram difference. The main ingredient of our method is a directed acyclic graph that records all relevant similarity information as follows: the regions of all time steps are the nodes of the graph, the edges represent possible short feature tracks between consecutive time steps, and the edge weights are given by the similarity of the connected regions. We compute a feature track as the global solution of a shortest path problem in the graph. We use these results to steer the - to the best of our knowledge - first algorithm for spatio-temporal feature similarity estimation. Our algorithm works for 2D and 3D time-dependent scalar fields. We compare our results to previous work, showcase its robustness to noise, and exemplify its utility using several real-world data sets.

Place, publisher, year, edition, pages
WILEY , 2017. Vol. 36, no 3, p. 1-11
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-211404DOI: 10.1111/cgf.13163ISI: 000404881200003Scopus ID: 2-s2.0-85022191409OAI: oai:DiVA.org:kth-211404DiVA, id: diva2:1129617
Conference
19th Eurographics/IEEE VGTC Conference on Visualization (EuroVis), JUN 12-16, 2017, Barcelona, SPAIN
Note

QC 20170804

Available from: 2017-08-04 Created: 2017-08-04 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. p. 55
Series
TRITA-CSC-A, ISSN 1653-5723 ; 2017:23
Keywords
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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Weinkauf, Tino

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