QR-Code Reconstruction from Event Data via Optimization in Code Subspace
Abstract
We propose an image reconstruction method from event data, assuming the target images belong to a prespecified class like QR codes. Instead of solving the reconstruction problem in the image space, we introduce a code space that covers all the noiseless target class images and solves the reconstruction problem on it. This restriction enormously reduces the number of optimizing parameters and makes the reconstruction problem well posed and robust to noise. We demonstrate fast and robust QR-code scanning in difficult, high-speed scenes with industrial high-speed cameras and other reconstruction methods.
Cite
Text
Nagata et al. "QR-Code Reconstruction from Event Data via Optimization in Code Subspace." Winter Conference on Applications of Computer Vision, 2020.Markdown
[Nagata et al. "QR-Code Reconstruction from Event Data via Optimization in Code Subspace." Winter Conference on Applications of Computer Vision, 2020.](https://mlanthology.org/wacv/2020/nagata2020wacv-qrcode/)BibTeX
@inproceedings{nagata2020wacv-qrcode,
title = {{QR-Code Reconstruction from Event Data via Optimization in Code Subspace}},
author = {Nagata, Jun and Sekikawa, Yusuke and Hara, Kosuke and Suzuki, Teppei and Aoki, Yoshimitsu},
booktitle = {Winter Conference on Applications of Computer Vision},
year = {2020},
url = {https://mlanthology.org/wacv/2020/nagata2020wacv-qrcode/}
}