DagRep.6.4.16.pdf
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Multidisciplinary Approaches to Multivalued Data: Modeling, Visualization, Analysis (Dagstuhl Seminar 16142)
Authors and all authors of the abstracts in this report
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Part of:
Issue:
Dagstuhl Reports, Volume 6, Issue 4
Volume: Dagstuhl Reports, Volume 6
Journal: Dagstuhl Reports (DagRep) - License:
Creative Commons Attribution 3.0 Unported license
- Publication date: 2016-10-05
Cite AsGet BibTex
Ingrid Hotz, Evren Özarslan, and Thomas Schultz. Multidisciplinary Approaches to Multivalued Data: Modeling, Visualization, Analysis (Dagstuhl Seminar 16142). In Dagstuhl Reports, Volume 6, Issue 4, pp. 16-38, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2016)
https://doi.org/10.4230/DagRep.6.4.16
https://doi.org/10.4230/DagRep.6.4.16
Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 16142, "Multidisciplinary Approaches to Multivalued Data: Modelling, Visualization, Analysis", which was attended by 27 international researchers, both junior and senior. Modelling multivalued data using tensors and higher-order descriptors has become common practice in neuroscience, engineering, and medicine. Novel tools for image analysis, visualization, as well as statistical hypothesis testing and machine learning are required to extract value from such data, and can only be developed within multidisciplinary collaborations. This report gathers abstracts of the talks held by participants on recent advances and open questions related to these challenges, as well as an account of topics raised within two of the breakout sessions.
Keywords
- visualization
- image processing
- statistical analysis
- machine learning
- tensor fields
- higher-order descriptors
- diffusion-weighted imaging (DWI)
- structural mechanics
- fluid dynamics
- microstructure imaging
- connectomics
- uncertainty visualization
- feature extraction
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