Swin transformer based siamese network for thermal and optical image registration

Name: Swin transformer based siamese network for thermal and optical image registration
Author: Elsaeidy, M., Yağmur, İsmail Can, Ateş, Hasan Fehmi, Güntürk, B. K.

İsim	Swin transformer based siamese network for thermal and optical image registration
Yazar	Elsaeidy, M., Yağmur, İsmail Can, Ateş, Hasan Fehmi, Güntürk, B. K.
Basım Tarihi:	2023
Basım Yeri	- IEEE
Konu	Keypoint, Multi-modal image registration, Transformer network
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	979-835034355-7
Kayıt Numarası	f35f8154-40ad-4cab-b26c-2f743dcdc550
Lokasyon	Computer Science
Tarih	2023
Örnek Metin	The process of multi-modal image registration is fundamental in remote sensing and visual navigation applications. However, existing image registration methods that are designed for single modality images do not provide satisfactory results when applied to multi-modal image registration. In this research, our objective is to achieve highly accurate alignment of both infrared and optical (visible range) images. To accomplish this goal, we explore the effectiveness of the Swin Transformer encoder and cosine loss in enhancing the keypoint-based image registration process. Simulation results show the improvement achieved in multi-modal registration by using a transformer based Siamese network.
DOI	10.1109/SIU59756.2023.10224035

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Swin transformer based siamese network for thermal and optical image registration

Yazar Elsaeidy, M., Yağmur, İsmail Can, Ateş, Hasan Fehmi, Güntürk, B. K.

Basım Tarihi 2023

Basım Yeri - IEEE

Konu Keypoint, Multi-modal image registration, Transformer network

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 979-835034355-7

Kayıt Numarası f35f8154-40ad-4cab-b26c-2f743dcdc550

Lokasyon Computer Science

Tarih 2023

Örnek Metin The process of multi-modal image registration is fundamental in remote sensing and visual navigation applications. However, existing image registration methods that are designed for single modality images do not provide satisfactory results when applied to multi-modal image registration. In this research, our objective is to achieve highly accurate alignment of both infrared and optical (visible range) images. To accomplish this goal, we explore the effectiveness of the Swin Transformer encoder and cosine loss in enhancing the keypoint-based image registration process. Simulation results show the improvement achieved in multi-modal registration by using a transformer based Siamese network.

DOI 10.1109/SIU59756.2023.10224035