Neural network identification of people hidden from view with a single-pixel, single-photon detector
Piergiorgio Caramazza, Alessandro Boccolini, Daniel Buschek, Matthias Hullin, Catherine F. Higham, Robert Henderson, Roderick Murray-Smith, Daniele Faccio
Scientific Reports (Nature Publishing Group), 8, 11945; doi: 10.1038/s41598-018-30390-0, 2018.
Abstract
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.Files
- Full Paper (PDF): CaramazzaEtAl-Neural-SREP2018.pdf
- Supplemental Document (PDF): CaramazzaEtAl-Neural-SREP2018-supp.pdf
BibTeX Citation
@article{caramazza2018srep,
title={Neural network identification of people hidden from view with a single-pixel, single-photon detector},
author={Caramazza, Piergiorgio and Boccolini, Alessandro and Buschek, Daniel and Hullin, Matthias and Higham, Catherine F and Henderson, Robert and Murray-Smith, Roderick and Faccio, Daniele},
journal={Scientific Reports},
volume={8},
number={1},
pages={11945},
year={2018},
publisher={Nature Publishing Group}
}