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Energy‐scale Aware Feature Extraction for Flow Visualization
Author(s) -
Pobitzer A.,
Tutkun M.,
Andreassen Ø.,
Fuchs R.,
Peikert R.,
Hauser H.
Publication year - 2011
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/j.1467-8659.2011.01926.x
Subject(s) - computer science , visualization , focus (optics) , flow (mathematics) , context (archaeology) , sort , feature (linguistics) , field (mathematics) , feature extraction , scale (ratio) , energy (signal processing) , process (computing) , algorithm , artificial intelligence , mathematics , mechanics , physics , statistics , quantum mechanics , information retrieval , pure mathematics , paleontology , linguistics , philosophy , optics , biology , operating system
In the visualization of flow simulation data, feature detectors often tend to result in overly rich response, making some sort of filtering or simplification necessary to convey meaningful images. In this paper we present an approach that builds upon a decomposition of the flow field according to dynamical importance of different scales of motion energy. Focusing on the high‐energy scales leads to a reduction of the flow field while retaining the underlying physical process. The presented method acknowledges the intrinsic structures of the flow according to its energy and therefore allows to focus on the energetically most interesting aspects of the flow. Our analysis shows that this approach can be used for methods based on both local feature extraction and particle integration and we provide a discussion of the error caused by the approximation. Finally, we illustrate the use of the proposed approach for both a local and a global feature detector and in the context of numerical flow simulations.

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