• First Order Locally Orderless...

Result: First Order Locally Orderless Registration

Title:
First Order Locally Orderless Registration
Authors:
Elmoataz, Abderrahim, Fadili, Jalal, Quéau, Yvain, Rabin, Julien, Simon, Loïc, Darkner, Sune, Vidarte, José D.T., Lauze, François
Source:
Darkner , S , Vidarte , J D T & Lauze , F 2021 , First Order Locally Orderless Registration . in A Elmoataz , J Fadili , Y Quéau , J Rabin & L Simon (eds) , Scale Space and Variational Methods in Computer Vision - 8th International Conference, SSVM 2021, Proceedings . Springer , Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , vol. 12679 LNCS , pp. 177-188 , 8th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2021 , Virtual, Online , 16/05/2021 .
Publisher Information:
Springer 2021
Document Type:
Electronic Resource Electronic Resource
Index Terms:
First order information, Image registration, Locally Orderless Images, contributionToPeriodical
URL:
https://researchprofiles.ku.dk/da/publications/first-order-locally-orderless-registration(5217ab04-66ad-4898-a09c-8c06780e3794).html
https://doi.org/10.1007/978-3-030-75549-2_15
https://curis.ku.dk/ws/files/313779179/2108.04926.pdf
Availability:
Open access content. Open access content
info:eu-repo/semantics/closedAccess
Note:
application/pdf
English
Other Numbers:
DAV oai:pure.atira.dk:publications/5217ab04-66ad-4898-a09c-8c06780e3794
urn:ISBN:9783030755485
1340141313
Contributing Source:
UNIV OF COPENHAGEN
From OAIster®, provided by the OCLC Cooperative.
Accession Number:
edsoai.on1340141313
Database:
OAIster

Further Information

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.