Distributed High Dimensional Information Theoretical Image Registration via Random Projections

TL;DR

This paper introduces a novel distributed image registration method that leverages random projections to efficiently estimate information theoretical measures in high-dimensional spaces, demonstrated through numerical experiments.

Contribution

It presents the first distributed, random projection-based approach for high-dimensional information theoretical image registration, improving computational efficiency.

Findings

Method is computationally efficient in high dimensions

First to combine distributed computing with RP for image registration

Numerical examples demonstrate effectiveness

Abstract

Information theoretical measures, such as entropy, mutual information, and various divergences, exhibit robust characteristics in image registration applications. However, the estimation of these quantities is computationally intensive in high dimensions. On the other hand, consistent estimation from pairwise distances of the sample points is possible, which suits random projection (RP) based low dimensional embeddings. We adapt the RP technique to this task by means of a simple ensemble method. To the best of our knowledge, this is the first distributed, RP based information theoretical image registration approach. The efficiency of the method is demonstrated through numerical examples.