VisIRNet: Deep image alignment for UAV-taken visible and infrared image Pairs

Name: VisIRNet: Deep image alignment for UAV-taken visible and infrared image Pairs
Author: Ndigande, Alain P., Ozer, Sedat

İsim	VisIRNet: Deep image alignment for UAV-taken visible and infrared image Pairs
Yazar	Ndigande, Alain P., Ozer, Sedat
Basım Tarihi:	2024-01-01
Basım Yeri	- IEEE
Konu	Unmanned aerial vehicle (UAV) image processing, Multimodal image registration, Lukas-kanade (LK) algorithms, Infrared image registration, Image alignment, Deep learning, Corner-matching, Computer architecture, Deep learning, Image resolution, Prediction algorithms, Autonomous aerial vehicles, Cameras, Feature extraction
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	0196-2892
Kayıt Numarası	15e104c3-e1f5-4c7c-952c-3eed8fdd8337
Lokasyon	International Relations
Tarih	2024-01-01
Örnek Metin	This article proposes a deep-learning-based solution for multimodal image alignment regarding unmanned aerial vehicle (UAV)-taken images. Many recently proposed state-of-the-art alignment techniques rely on using Lucas-Kanade (LK)-based solutions for a successful alignment. However, we show that we can achieve state-of-the-art results without using LK-based methods. Our approach carefully utilizes a two-branch-based convolutional neural network (CNN) based on feature embedding blocks. We propose two variants of our approach, where in the first variant (Model A), we directly predict the new coordinates of only the four corners of the image to be aligned; and in the second one (Model B), we predict the homography matrix directly. Applying alignment on the image corners forces the algorithm to match only those four corners as opposed to computing and matching many (key) points, since the latter may cause many outliers, yielding less accurate alignment. We test our proposed approach on four aerial datasets and obtain state-of-the-art results when compared to the existing recent deep LK-based architectures.
DOI	10.1109/TGRS.2024.3367986
Cilt	62

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VisIRNet: Deep image alignment for UAV-taken visible and infrared image Pairs

Yazar Ndigande, Alain P., Ozer, Sedat

Basım Tarihi 2024-01-01

Basım Yeri - IEEE

Konu Unmanned aerial vehicle (UAV) image processing, Multimodal image registration, Lukas-kanade (LK) algorithms, Infrared image registration, Image alignment, Deep learning, Corner-matching, Computer architecture, Deep learning, Image resolution, Prediction algorithms, Autonomous aerial vehicles, Cameras, Feature extraction

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 0196-2892

Kayıt Numarası 15e104c3-e1f5-4c7c-952c-3eed8fdd8337

Lokasyon International Relations

Tarih 2024-01-01

Örnek Metin This article proposes a deep-learning-based solution for multimodal image alignment regarding unmanned aerial vehicle (UAV)-taken images. Many recently proposed state-of-the-art alignment techniques rely on using Lucas-Kanade (LK)-based solutions for a successful alignment. However, we show that we can achieve state-of-the-art results without using LK-based methods. Our approach carefully utilizes a two-branch-based convolutional neural network (CNN) based on feature embedding blocks. We propose two variants of our approach, where in the first variant (Model A), we directly predict the new coordinates of only the four corners of the image to be aligned; and in the second one (Model B), we predict the homography matrix directly. Applying alignment on the image corners forces the algorithm to match only those four corners as opposed to computing and matching many (key) points, since the latter may cause many outliers, yielding less accurate alignment. We test our proposed approach on four aerial datasets and obtain state-of-the-art results when compared to the existing recent deep LK-based architectures.

DOI 10.1109/TGRS.2024.3367986

Cilt 62