Object classification through scattering media with deep learning on time resolved measurement
Name
oe-25-15-17466.pdf
Description
Published version
Size
10.19 MB
Format
Adobe PDF
Checksum (MD5)
b62af5a19bb6168b23dc0726029cd99f
Author(s)
Satat, Guy
Tancik, Matthew
Gupta, Otkrist
Heshmat, Barmak
Raskar, Ramesh
Date Issued
2017
Journal
Optics Express
Publisher
The Optical Society
Version
Final published version
Abstract
© 2017 Optical Society of America. We demonstrate an imaging technique that allows identification and classification of objects hidden behind scattering media and is invariant to changes in calibration parameters within a training range. Traditional techniques to image through scattering solve an inverse problem and are limited by the need to tune a forward model with multiple calibration parameters (like camera field of view, illumination position etc.). Instead of tuning a forward model and directly inverting the optical scattering, we use a data driven approach and leverage convolutional neural networks (CNN) to learn a model that is invariant to calibration parameters variations within the training range and nearly invariant beyond that. This effectively allows robust imaging through scattering conditions that is not sensitive to calibration. The CNN is trained with a large synthetic dataset generated with a Monte Carlo (MC) model that contains random realizations of major calibration parameters. The method is evaluated with a time-resolved camera and multiple experimental results are provided including pose estimation of a mannequin hidden behind a paper sheet with 23 correct classifications out of 30 tests in three poses (76.6% accuracy on real-world measurements). This approach paves the way towards real-time practical non line of sight (NLOS) imaging applications.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1364/OE.25.017466
