Image Registration of Very Large Images via Genetic Programming

TL;DR

This paper introduces a genetic programming-based method for image registration that effectively handles very large images without assuming specific transformation models, accommodating complex distortions.

Contribution

The paper presents a novel GP framework for IR that adapts to large images and complex transformations without prior assumptions, improving registration accuracy.

Findings

Effective registration of very large images achieved

Flexible handling of complex transformations demonstrated

No prior transformation assumptions required

Abstract

Image registration (IR) is a fundamental task in image processing for matching two or more images of the same scene taken at different times, from different viewpoints and/or by different sensors. Due to the enormous diversity of IR applications, automatic IR remains a challenging problem to this day. A wide range of techniques has been developed for various data types and problems. However, they might not handle effectively very large images, which give rise usually to more complex transformations, e.g., deformations and various other distortions. In this paper we present a genetic programming (GP)-based approach for IR, which could offer a significant advantage in dealing with very large images, as it does not make any prior assumptions about the transformation model. Thus, by incorporating certain generic building blocks into the proposed GP framework, we hope to realize a large set of specialized transformations that should yield accurate registration of very large images.