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arXiv · 1801.05171

Deep Multi-Spectral Registration Using Invariant Descriptor Learning

Nati Ofir·Shai Silberstein·Hila Levi·Dani Rozenbaum·Yosi Keller·Sharon Duvdevani Bar
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Abstract

In this paper, we introduce a novel deep-learning method to align cross-spectral images. Our approach relies on a learned descriptor which is invariant to different spectra. Multi-modal images of the same scene capture different signals and therefore their registration is challenging and it is not solved by classic approaches. To that end, we developed a feature-based approach that solves the visible (VIS) to Near-Infra-Red (NIR) registration problem. Our algorithm detects corners by Harris and matches them by a patch-metric learned on top of CIFAR-10 network descriptor. As our experiments demonstrate we achieve a high-quality alignment of cross-spectral images with a sub-pixel accuracy. Comparing to other existing methods, our approach is more accurate in the task of VIS to NIR registration.

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Nati Ofir, Shai Silberstein, Hila Levi, Dani Rozenbaum, Yosi Keller, Sharon Duvdevani Bar. 2018-01-16. Deep Multi-Spectral Registration Using Invariant Descriptor Learning. https://doi.org/10.1109/icip.2018.8451640

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