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

Title	Improved homographic adaptation for keypoint generation in cross-spectral registration of thermal and optical imagery
Author	Yağmur, İsmail Can, Ateş, Hasan Fehmi
Publication Date:	2023
Publication Place	- SPIE
Subject	Homographic adaptation, Image registration, Multi-spectral, Robot vision
Type	Document
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	978-151066695-5
Record ID	55c577f8-7a8f-40c7-adb7-7b044f3d3536
Library Location	Computer Science
Date	2023
Sample Text	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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Özyeğin University

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

Author Yağmur, İsmail Can, Ateş, Hasan Fehmi

Publication Date 2023

Publication Place - SPIE

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

Type Document

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 978-151066695-5

Record ID 55c577f8-7a8f-40c7-adb7-7b044f3d3536

Library Location Computer Science

Date 2023

Sample Text 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