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Comparison and Tracking Methods for Interactive Visualization of Topological Structures in Scalar Fields
KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). (Visualization)
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 [en]
topology, scalar fields, merge tree, tree comparison, tracking, similarity search
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-216375ISBN: 978-91-7729-580-8 (print)OAI: oai:DiVA.org:kth-216375DiVA, id: diva2:1150643
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
List of papers
1. Extended Branch Decomposition Graphs: Structural Comparison of Scalar Data
Open this publication in new window or tab >>Extended Branch Decomposition Graphs: Structural Comparison of Scalar Data
2014 (English)In: Computer Graphics Forum (Proc. EuroVis), ISSN 1467-8659, Vol. 33, no 3, p. 41-50Article in journal (Refereed) Published
Abstract [en]

We present a method to find repeating topological structures in scalar data sets. More precisely, we compare all subtrees of two merge trees against each other - in an efficient manner exploiting redundancy. This provides pair-wise distances between the topological structures defined by sub/superlevel sets, which can be exploited in several applications such as finding similar structures in the same data set, assessing periodic behavior in time-dependent data, and comparing the topology of two different data sets. To do so, we introduce a novel data structure called the extended branch decomposition graph, which is composed of the branch decompositions of all subtrees of the merge tree. Based on dynamic programming, we provide two highly efficient algorithms for computing and comparing extended branch decomposition graphs. Several applications attest to the utility of our method and its robustness against noise.

Place, publisher, year, edition, pages
Wiley-Blackwell, 2014
National Category
Computer Sciences
Research subject
Computer Science; SRA - E-Science (SeRC)
Identifiers
urn:nbn:se:kth:diva-184831 (URN)10.1111/cgf.12360 (DOI)000340597400005 ()2-s2.0-84904414532 (Scopus ID)
Note

QC 20160406

Available from: 2016-04-05 Created: 2016-04-05 Last updated: 2018-01-10Bibliographically approved
2. Fast Similarity Search in Scalar Fields using Merging Histograms
Open this publication in new window or tab >>Fast Similarity Search in Scalar Fields using Merging Histograms
2015 (English)In: Topological Methods in Data Analysis and Visualization IV: Theory, Algorithms, and Applications, Springer, 2015, p. 121-134Chapter in book (Refereed)
Abstract [en]

Similarity estimation in scalar fields using level set topology has attracted a lot of attention in the recent past. Most existing techniques match parts of contour or merge trees against each other by estimating a best overlap between them. Due to their combinatorial nature, these methods can be computationally expensive or prone to instabilities. In this paper, we use an inexpensive feature descriptor to compare subtrees of merge trees against each other. It is the data histogram of the voxels encompassed by a subtree. A small modification of the merge tree computation algorithm allows for obtaining these histograms very efficiently. Furthermore, the descriptor is robust against instabilities in the merge tree. The method is useful in an interactive environment, where a user can search for all structures similar to an interactively selected one. Our method is conservative in the sense that it finds all similar structures, with the rare occurrence of some false positives. We show with several examples the effectiveness, efficiency and accuracy of our method.

Place, publisher, year, edition, pages
Springer, 2015
National Category
Computer Sciences
Research subject
Computer Science; SRA - E-Science (SeRC)
Identifiers
urn:nbn:se:kth:diva-213972 (URN)10.1007/978-3-319-44684-4_7 (DOI)2-s2.0-85020191758 (Scopus ID)9783319446820 (ISBN)
Conference
TopoInVis 2015, May 20-22
Note

QC 20160406

Available from: 2017-09-07 Created: 2017-09-07 Last updated: 2018-01-13Bibliographically approved
3. Global Feature Tracking and Similarity Estimation in Time-Dependent Scalar Fields
Open this publication in new window or tab >>Global Feature Tracking and Similarity Estimation in Time-Dependent Scalar Fields
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
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:kth:diva-211404 (URN)10.1111/cgf.13163 (DOI)000404881200003 ()2-s2.0-85022191409 (Scopus ID)
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
4. Fast Topology-based Feature Tracking using a Directed Acyclic Graph
Open this publication in new window or tab >>Fast Topology-based Feature Tracking using a Directed Acyclic Graph
(English)Manuscript (preprint) (Other academic)
National Category
Computer Sciences
Identifiers
urn:nbn:se:kth:diva-213965 (URN)
Note

QC 20171020

Available from: 2017-09-07 Created: 2017-09-07 Last updated: 2018-01-13Bibliographically approved

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