Abstract
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.
Originalsprog | Engelsk |
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Titel | Scale Space and Variational Methods in Computer Vision - 8th International Conference, SSVM 2021, Proceedings |
Redaktører | Abderrahim Elmoataz, Jalal Fadili, Yvain Quéau, Julien Rabin, Loïc Simon |
Forlag | Springer |
Publikationsdato | 2021 |
Sider | 177-188 |
ISBN (Trykt) | 9783030755485 |
DOI | |
Status | Udgivet - 2021 |
Begivenhed | 8th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2021 - Virtual, Online Varighed: 16 maj 2021 → 20 maj 2021 |
Konference
Konference | 8th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2021 |
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By | Virtual, Online |
Periode | 16/05/2021 → 20/05/2021 |
Navn | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Vol/bind | 12679 LNCS |
ISSN | 0302-9743 |
Bibliografisk note
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