@inproceedings{326e8346ae8a4215a4b3df8b89dfa827,
title = "Deep multi-spectral registration using invariant descriptor learning",
abstract = "In this work, we propose a deep-learning approach for aligning cross-spectral images. Our approach utilizes a learned descriptor invariant to different spectra. Multi-modal images of the same scene capture different characteristics and therefore their registration is challenging. To that end, we developed a feature-based approach for registering visible (VIS) to Near-Infra-Red (NIR) images. Our scheme detects corners by Harris and matches them by a patch-metric learned on top of a network trained using the CIFAR-10 dataset. As our experiments demonstrate, we achieve accurate alignment of cross-spectral images with sub-pixel accuracy. Comparing to contemporary state-of-the-art, our approach is more accurate in the task of VIS to NIR registration.",
keywords = "Deep-Learning, Image Registration, Multi-Spectral Imaging",
author = "Nati Ofir and Shai Silberstein and Hila Levi and Dani Rozenbaum and Yosi Keller and {Duvdevani Bar}, Sharon",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 25th IEEE International Conference on Image Processing, ICIP 2018 ; Conference date: 07-10-2018 Through 10-10-2018",
year = "2018",
month = aug,
day = "29",
doi = "https://doi.org/10.1109/ICIP.2018.8451640",
language = "الإنجليزيّة",
series = "Proceedings - International Conference on Image Processing, ICIP",
publisher = "IEEE Computer Society",
pages = "1238--1242",
booktitle = "2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings",
address = "الولايات المتّحدة",
}