Distributed High Dimensional Information Theoretical Image Registration via Random Projections
This work addresses a computational bottleneck in image registration for researchers and practitioners, though it appears incremental as it builds on existing random projection methods.
The paper tackles the computational intensity of estimating information theoretical measures for high-dimensional image registration by adapting random projection techniques with an ensemble method, demonstrating efficiency through numerical examples.
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.