Machine learning (ML) has been widely applied to image classification. Here, we extend this application to data generated by a camera comprised of only a standard CMOS image sensor with no lens. We first created a database of lensless images of handwritten digits. Then, we trained a ML algorithm on this dataset. Finally, we demonstrated that the trained ML algorithm is able to classify the digits with accuracy as high as 99% for 2 digits. Our approach clearly demonstrates the potential for non-human cameras in machine-based decision-making scenarios.
@article{arxiv.1709.00408,
title = {Lensless-camera based machine learning for image classification},
author = {Ganghun Kim and Stefan Kapetanovic and Rachael Palmer and Rajesh Menon},
journal= {arXiv preprint arXiv:1709.00408},
year = {2017}
}