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231225s2019 xx |||||o 00| ||eng c |
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|a 10.1109/MCG.2018.2881523
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|a eng
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| 100 |
1 |
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|a Han, Jun
|e verfasserin
|4 aut
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| 245 |
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|a Flow Field Reduction Via Reconstructing Vector Data From 3-D Streamlines Using Deep Learning
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| 264 |
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|c 2019
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 23.07.2019
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|a published: Print
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a We present a new approach for streamline-based flow field representation and reduction. Our method can work in the in situ visualization setting by tracing streamlines from each time step of the simulation and storing compressed streamlines for post hoc analysis where users can afford longer reconstruction time for higher reconstruction quality using decompressed streamlines. At the heart of our approach is a deep learning method for vector field reconstruction that takes the streamlines traced from the original vector fields as input and applies a two-stage process to reconstruct high-quality vector fields. To demonstrate the effectiveness of our approach, we show qualitative and quantitative results with several data sets and compare our method against the de facto method of gradient vector flow in terms of speed and quality tradeoff
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| 650 |
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|a Journal Article
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| 700 |
1 |
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|a Tao, Jun
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zheng, Hao
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Guo, Hanqi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Chen, Danny Z
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Chaoli
|e verfasserin
|4 aut
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|i Enthalten in
|t IEEE computer graphics and applications
|d 1997
|g 39(2019), 4 vom: 25. Juli, Seite 54-67
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|x 1558-1756
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|g volume:39
|g year:2019
|g number:4
|g day:25
|g month:07
|g pages:54-67
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|u http://dx.doi.org/10.1109/MCG.2018.2881523
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