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Feature Level-Sets: Generalizing Iso-surfaces to Multi-variate Data
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. (Scientific Visualization)ORCID iD: 0000-0002-8324-550X
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. (Scientific Visualization)ORCID iD: 0000-0001-7285-0483
2018 (English)In: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506Article in journal (Refereed) Epub ahead of print
Abstract [en]

Iso-surfaces or level-sets provide an effective and frequently used means for feature visualization. However, they are restricted to simple features for uni-variate data. The approach does not scale when moving to multi-variate data or when considering more complex feature definitions. In this paper, we introduce the concept of traits and feature level-sets, which can be understood as a generalization of level-sets as it includes iso-surfaces, and fiber surfaces as special cases. The concept is applicable to a large class of traits defined as subsets in attribute space, which can be arbitrary combinations of points, lines, surfaces and volumes.  It is implemented into a system that provides an interface to define traits in an interactive way and multiple rendering options. We demonstrate the effectiveness of the approach using multi-variate data sets of different nature, including vector and tensor data, from different application domains.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018.
National Category
Other Computer and Information Science
Identifiers
URN: urn:nbn:se:liu:diva-151231DOI: 10.1109/TVCG.2018.2867488PubMedID: 30183637Scopus ID: 2-s2.0-85052841941OAI: oai:DiVA.org:liu-151231DiVA, id: diva2:1247897
Funder
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsAvailable from: 2018-09-13 Created: 2018-09-13 Last updated: 2019-11-11Bibliographically approved

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