Rot-Net: A Rotation Angle Detector between Multi-modal Images Using Siamese Network

Yi-Hang Huang; Ze-Yi Li; Haitao Zhang; Weiwei Zhao; Yan Wang

doi:10.1364/AIS.2021.JTh6A.42

OSA Optical Sensors and Sensing Congress 2021 (AIS, FTS, HISE, SENSORS, ES)
OSA Technical Digest (Optica Publishing Group, 2021),
paper JTh6A.42
•https://doi.org/10.1364/AIS.2021.JTh6A.42

Rot-Net: A Rotation Angle Detector between Multi-modal Images Using Siamese Network

Yi-Hang Huang, Ze-Yi Li, Haitao Zhang, Weiwei Zhao, and Yan Wang

Fourier Transform Spectroscopy 2021

Washington, DC United States
19–23 July 2021
ISBN: 978-1-943580-90-3

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Joint Poster Session III (JTh6A)

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Y. Huang, Z. Li, H. Zhang, W. Zhao, and Y. Wang, "Rot-Net: A Rotation Angle Detector between Multi-modal Images Using Siamese Network," in OSA Optical Sensors and Sensing Congress 2021 (AIS, FTS, HISE, SENSORS, ES), S. Buckley, F. Vanier, S. Shi, K. Walker, I. Coddington, S. Paine, K. Lok Chan, W. Moses, S. Qian, P. Pellegrino, F. Vollmer, G. , J. Jágerská, R. Menzies, L. Emmenegger, and J. Westberg, eds., OSA Technical Digest (Optica Publishing Group, 2021), paper JTh6A.42.

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Abstract

We construct an efficient learning framework to predict and correct the difference angle between multi-modal images without any additional auxiliary information. As a preprocessing of image registration, our network has good performance through multi-precision tests.

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