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  • 标题:Neural network identification of people hidden from view with a single-pixel, single-photon detector
  • 本地全文:下载
  • 作者:Piergiorgio Caramazza ; Alessandro Boccolini ; Daniel Buschek
  • 期刊名称:Scientific Reports
  • 电子版ISSN:2045-2322
  • 出版年度:2018
  • 卷号:8
  • 期号:1
  • 页码:11945
  • DOI:10.1038/s41598-018-30390-0
  • 语种:English
  • 出版社:Springer Nature
  • 摘要:Light scattered from multiple surfaces can be used to retrieve information of hidden environments. However, full three-dimensional retrieval of an object hidden from view by a wall has only been achieved with scanning systems and requires intensive computational processing of the retrieved data. Here we use a non-scanning, single-photon single-pixel detector in combination with a deep convolutional artificial neural network: this allows us to locate the position and to also simultaneously provide the actual identity of a hidden person, chosen from a database of people (N = 3). Artificial neural networks applied to specific computational imaging problems can therefore enable novel imaging capabilities with hugely simplified hardware and processing times.
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