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231224s2014 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2014.2344294
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|a eng
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| 100 |
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|a Yang, Jianbo
|e verfasserin
|4 aut
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| 245 |
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|a Video compressive sensing using Gaussian mixture models
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|c 2014
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|a Text
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|a ƒaComputermedien
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|a Date Completed 30.03.2015
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|a Date Revised 08.10.2014
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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 A Gaussian mixture model (GMM)-based algorithm is proposed for video reconstruction from temporally compressed video measurements. The GMM is used to model spatio-temporal video patches, and the reconstruction can be efficiently computed based on analytic expressions. The GMM-based inversion method benefits from online adaptive learning and parallel computation. We demonstrate the efficacy of the proposed inversion method with videos reconstructed from simulated compressive video measurements, and from a real compressive video camera. We also use the GMM as a tool to investigate adaptive video compressive sensing, i.e., adaptive rate of temporal compression
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|a Journal Article
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| 700 |
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|a Yuan, Xin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Liao, Xuejun
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Llull, Patrick
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Brady, David J
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Sapiro, Guillermo
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Carin, Lawrence
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 23(2014), 11 vom: 01. Nov., Seite 4863-78
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|g pages:4863-78
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|u http://dx.doi.org/10.1109/TIP.2014.2344294
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