On Learning from Ghost Imaging without Imaging
Image and Video Processing
2019-05-30 v5 Machine Learning
Machine Learning
Abstract
Computational ghost imaging is an imaging technique in which an object is imaged from light collected using a single-pixel detector with no spatial resolution. Recently, ghost cytometry has been proposed for a high-speed cell-classification method that involves ghost imaging and machine learning in flow cytometry. Ghost cytometry skips the reconstruction of cell images from signals and directly used signals for cell-classification because this reconstruction is what creates the bottleneck in the high-speed analysis. In this paper, we provide theoretical analysis for learning from ghost imaging without imaging.
Cite
@article{arxiv.1903.06009,
title = {On Learning from Ghost Imaging without Imaging},
author = {Issei Sato},
journal= {arXiv preprint arXiv:1903.06009},
year = {2019}
}