Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Fast Similarity Search in Scalar Fields using Merging Histograms
KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). (Visualization and Data Analysis)
KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). (Visualization and Data Analysis)ORCID iD: 0000-0002-1498-9062
2015 (English)In: Topological Methods in Data Analysis and Visualization IV: Theory, Algorithms, and Applications, Springer, 2015, 121-134 p.Chapter in book (Refereed)
Resource type
Text
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. 121-134 p.
National Category
Computer Sciences
Research subject
Computer Science; SRA - E-Science (SeRC)
Identifiers
URN: urn:nbn:se:kth:diva-213972DOI: 10.1007/978-3-319-44684-4_7Scopus ID: 2-s2.0-85020191758ISBN: 9783319446820 (print)OAI: oai:DiVA.org:kth-213972DiVA: diva2:1139377
Conference
TopoInVis 2015, May 20-22
Note

QC 20160406

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

Open Access in DiVA

No full text

Other links

Publisher's full textScopus

Authority records BETA

Weinkauf, Tino

Search in DiVA

By author/editor
Saikia, HimangshuWeinkauf, Tino
By organisation
Computational Science and Technology (CST)
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 20 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf