On the (Un)Suitability of Strict Feature Definitions for Uncertain Data

Weinkauf, Tino

doi:10.1007/978-1-4471-6497-5_4

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On the (Un)Suitability of Strict Feature Definitions for Uncertain Data

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Weinkauf, Tino
Computer Graphics, MPI for Informatics, Max Planck Society;

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Citation

Weinkauf, T. (2014). On the (Un)Suitability of Strict Feature Definitions for Uncertain Data. In C. D. Hansen, M. Chen, C. R. Johnson, A. E. Kaufman, & H. Hagen (Eds.), Scientific Visualization (pp. 45-50). London: Springer. doi:10.1007/978-1-4471-6497-5_4.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0024-5315-F

Abstract

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.