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|a 10.1109/TVCG.2020.2984413
|2 doi
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|a eng
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1 |
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|a Bhatia, Harsh
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Vector Field Decompositions Using Multiscale Poisson Kernel
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| 264 |
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|c 2021
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|a Text
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|a ƒaComputermedien
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|a Date Revised 30.07.2021
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Extraction of multiscale features using scale-space is one of the fundamental approaches to analyze scalar fields. However, similar techniques for vector fields are much less common, even though it is well known that, for example, turbulent flows contain cascades of nested vortices at different scales. The challenge is that the ideas related to scale-space are based upon iteratively smoothing the data to extract features at progressively larger scale, making it difficult to extract overlapping features. Instead, we consider spatial regions of influence in vector fields as scale, and introduce a new approach for the multiscale analysis of vector fields. Rather than smoothing the flow, we use the natural Helmholtz-Hodge decomposition to split it into small-scale and large-scale components using progressively larger neighborhoods. Our approach creates a natural separation of features by extracting local flow behavior, for example, a small vortex, from large-scale effects, for example, a background flow. We demonstrate our technique on large-scale, turbulent flows, and show multiscale features that cannot be extracted using state-of-the-art techniques
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|a Journal Article
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| 700 |
1 |
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|a Kirby, Robert M
|e verfasserin
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| 700 |
1 |
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|a Pascucci, Valerio
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Bremer, Peer-Timo
|e verfasserin
|4 aut
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|i Enthalten in
|t IEEE transactions on visualization and computer graphics
|d 1996
|g 27(2021), 9 vom: 31. Sept., Seite 3781-3793
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