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231224s2016 xx |||||o 00| ||eng c |
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|a 10.1109/TVCG.2015.2467292
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|a eng
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| 100 |
1 |
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|a Wang, Zhongjie
|e verfasserin
|4 aut
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| 245 |
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|a Multi-field Pattern Matching based on Sparse Feature Sampling
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| 264 |
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|c 2016
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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| 500 |
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|a Date Completed 05.02.2016
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|a Date Revised 04.11.2015
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a We present an approach to pattern matching in 3D multi-field scalar data. Existing pattern matching algorithms work on single scalar or vector fields only, yet many numerical simulations output multi-field data where only a joint analysis of multiple fields describes the underlying phenomenon fully. Our method takes this into account by bundling information from multiple fields into the description of a pattern. First, we extract a sparse set of features for each 3D scalar field using the 3D SIFT algorithm (Scale-Invariant Feature Transform). This allows for a memory-saving description of prominent features in the data with invariance to translation, rotation, and scaling. Second, the user defines a pattern as a set of SIFT features in multiple fields by e.g. brushing a region of interest. Third, we locate and rank matching patterns in the entire data set. Experiments show that our algorithm is efficient in terms of required memory and computational efforts
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| 650 |
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|a Journal Article
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| 700 |
1 |
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|a Seidel, Hans-Peter
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Weinkauf, Tino
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on visualization and computer graphics
|d 1996
|g 22(2016), 1 vom: 18. Jan., Seite 807-16
|w (DE-627)NLM098269445
|x 1941-0506
|7 nnns
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| 773 |
1 |
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|g volume:22
|g year:2016
|g number:1
|g day:18
|g month:01
|g pages:807-16
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|u http://dx.doi.org/10.1109/TVCG.2015.2467292
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