First Order Locally Orderless Registration

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

First Order Locally Orderless Registration (FLOR) is a scale-space framework for image density estimation used for defining image similarity, mainly for Image Registration. The Locally Orderless Registration framework was designed in principle to use zeroth-order information, providing image density estimates over three scales: image scale, intensity scale, and integration scale. We extend it to take first-order information into account and hint at higher-order information. We show how standard similarity measures extend into the framework. We study especially Sum of Squared Differences (SSD) and Normalized Cross-Correlation (NCC) but present the theory of how Normalised Mutual Information (NMI) can be included.

OriginalsprogEngelsk
TitelScale Space and Variational Methods in Computer Vision - 8th International Conference, SSVM 2021, Proceedings
RedaktørerAbderrahim Elmoataz, Jalal Fadili, Yvain Quéau, Julien Rabin, Loïc Simon
ForlagSpringer
Publikationsdato2021
Sider177-188
ISBN (Trykt)9783030755485
DOI
StatusUdgivet - 2021
Begivenhed8th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2021 - Virtual, Online
Varighed: 16 maj 202120 maj 2021

Konference

Konference8th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2021
ByVirtual, Online
Periode16/05/202120/05/2021
NavnLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Vol/bind12679 LNCS
ISSN0302-9743

ID: 283137220