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.
Cite
@article{arxiv.2108.04926,
title = {First Order Locally Orderless Registration},
author = {Sune Darkner and Jose D Tascon and Francois Lauze},
journal= {arXiv preprint arXiv:2108.04926},
year = {2021}
}