First Order Locally Orderless Registration
This work provides an incremental improvement for researchers in medical imaging or computer vision by enhancing image similarity estimation for registration tasks.
The authors tackled the problem of image registration by extending the Locally Orderless Registration framework to incorporate first-order information, showing how standard similarity measures like SSD and NCC can be adapted within this new scale-space approach.
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.