# First Order Locally Orderless Registration

arXiv: https://arxiv.org/abs/2108.04926 · HTML: https://mlsanity.com/paper/2108.04926/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 16 references that are in the map.
- First posted on arXiv 10 Aug 2021.

## Built on by (most cited first, 1 of 1)

- [Locally orderless networks](https://mlsanity.com/paper/2406.13514/) (2024)

## Abstract (arXiv)

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.