Improved homographic adaptation for keypoint generation in cross-spectral registration of thermal and optical imagery

Name: Improved homographic adaptation for keypoint generation in cross-spectral registration of thermal and optical imagery
Author: Yağmur, İsmail Can, Ateş, Hasan Fehmi

İsim	Improved homographic adaptation for keypoint generation in cross-spectral registration of thermal and optical imagery
Yazar	Yağmur, İsmail Can, Ateş, Hasan Fehmi
Basım Tarihi:	2023
Basım Yeri	- SPIE
Konu	Homographic adaptation, Image registration, Multi-spectral, Robot vision
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-151066695-5
Kayıt Numarası	55c577f8-7a8f-40c7-adb7-7b044f3d3536
Lokasyon	Computer Science
Tarih	2023
Örnek Metin	Autonomous navigation is an important area of research for aerial vehicles. Visual odometry and simultaneous localization and mapping algorithms are critical for the three-dimensional understanding of the environment. For that purpose, consistent multi-spectral maps of the environment should be generated. Existing pixel-based image registration methods are accurate but too slow to operate in real-time. Recently deep learning is used to develop feature-based data-driven methods for generating interest points and associated descriptors for registering multi-spectral image pairs. These methods are fast and perform better than existing methods for optical images. However, the results are less convincing for thermal image registration. In this work, we propose an improved multi-spectral homographic adaptation technique to generate highly repeatable ground truth interest points that are invariant across viewpoint changes in both spectra. These interest points are used to train the MultiPoint image registration network. Simulation results show that our improved model outperforms existing techniques for feature-based image alignment of optical and thermal images.
DOI	10.1117/12.2678307
Cilt	12733

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Improved homographic adaptation for keypoint generation in cross-spectral registration of thermal and optical imagery

Yazar Yağmur, İsmail Can, Ateş, Hasan Fehmi

Basım Tarihi 2023

Basım Yeri - SPIE

Konu Homographic adaptation, Image registration, Multi-spectral, Robot vision

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-151066695-5

Kayıt Numarası 55c577f8-7a8f-40c7-adb7-7b044f3d3536

Lokasyon Computer Science

Tarih 2023

Örnek Metin Autonomous navigation is an important area of research for aerial vehicles. Visual odometry and simultaneous localization and mapping algorithms are critical for the three-dimensional understanding of the environment. For that purpose, consistent multi-spectral maps of the environment should be generated. Existing pixel-based image registration methods are accurate but too slow to operate in real-time. Recently deep learning is used to develop feature-based data-driven methods for generating interest points and associated descriptors for registering multi-spectral image pairs. These methods are fast and perform better than existing methods for optical images. However, the results are less convincing for thermal image registration. In this work, we propose an improved multi-spectral homographic adaptation technique to generate highly repeatable ground truth interest points that are invariant across viewpoint changes in both spectra. These interest points are used to train the MultiPoint image registration network. Simulation results show that our improved model outperforms existing techniques for feature-based image alignment of optical and thermal images.

DOI 10.1117/12.2678307

Cilt 12733