Coded Illumination and Imaging for Fluorescence Based Classification
Yuta Asano, Misaki Meguro, Chao Wang, Antony Lam, Yinqiang Zheng, Takahiro Okabe, Imari Sato
Abstract
The quick detection of specific substances in objects such as produce items via non-destructive visual cues is vital to ensuring the quality and safety of consumer products. At the same time, it is well-known that the fluorescence excitation-emission characteristics of many organic objects can serve as a kind of ``fingerprint'' for detecting the presence of specific substances in classification tasks such as determining if something is safe to consume. However, conventional capture of the fluorescence excitation-emission matrix can take on the order of minutes and can only be done for point measurements. In this paper, we propose a coded illumination approach whereby light spectra are learned such that key visual fluorescent features can be easily seen for material classification. We show that under a single coded illuminant, we can capture one RGB image and perform pixel-level classifications of materials at high accuracy. This is demonstrated through effective classification of different types of honey and alcohol using real images.
BibTeX
@inproceedings{eccv2018_codedilluminatio,
title = {Coded Illumination and Imaging for Fluorescence Based Classification},
author = {Yuta Asano and Misaki Meguro and Chao Wang and Antony Lam and Yinqiang Zheng and Takahiro Okabe and Imari Sato},
booktitle = {ECCV 2018},
year = {2018}
}