Feature Level-Sets : Generalizing Iso-surfaces to Multi-variate Data
| Autor: | Jochen Jankowai, Ingrid Hotz |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Theoretical computer science
Computer science Generalization business.industry Feature extraction 020207 software engineering 02 engineering and technology Computer Graphics and Computer-Aided Design Transfer function Annan data- och informationsvetenskap Rendering (computer graphics) Visualization Data visualization Random variate Signal Processing 0202 electrical engineering electronic engineering information engineering Computer Vision and Pattern Recognition Tensor business Other Computer and Information Science Software |
| Popis: | 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. Funding agencies: ELLIIT program; Swedish eScience Research Centre (SeRC) |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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