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On the (Un)Suitability of Strict Feature Definitions for Uncertain Data
KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). Max Planck Institute for Informatics. (Visualization and Data Analysis)ORCID iD: 0000-0002-1498-9062
2014 (English)In: Scientific Visualization: Uncertainty, Multifield, Biomedical, and Scalable Visualization / [ed] Chen, M.; Hagen, H.; Hansen, C.; Johnson, C.; Kaufmann, A., Springer , 2014Chapter in book (Other academic)Text
Abstract [en]

We discuss strategies to successfully work with strict feature definitions such as topology in the presence of noisy/uncertain data. To that end, we review previous work from the literature and identify three strategies: the development of fuzzy analogs to strict feature definitions, the aggregation of features, and the filtering of features. Regarding the latter, we will present a detailed discussion of filtering ridges/valleys and topological structures.

Place, publisher, year, edition, pages
Springer , 2014.
National Category
Computer Science
Research subject
Computer Science; SRA - E-Science (SeRC)
URN: urn:nbn:se:kth:diva-184835OAI: diva2:916906

QC 20160405

Available from: 2016-04-05 Created: 2016-04-05 Last updated: 2016-04-05Bibliographically approved

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