On Learning from Ghost Imaging without Imaging
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.
- Publication:
-
arXiv e-prints
- Pub Date:
- March 2019
- DOI:
- 10.48550/arXiv.1903.06009
- arXiv:
- arXiv:1903.06009
- Bibcode:
- 2019arXiv190306009S
- Keywords:
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- Electrical Engineering and Systems Science - Image and Video Processing;
- Computer Science - Machine Learning;
- Statistics - Machine Learning