Affine Image Registration Transformation Estimation Using a Real Coded Genetic Algorithm with SBX
This work addresses image registration for computer vision applications, presenting an incremental improvement through a hybrid optimization approach.
The paper tackles the problem of aligning 2D images via affine transformation estimation using a real-coded genetic algorithm with Simulated Binary Crossover (SBX) and a novel randomized point-matching technique that introduces noise. The results show competitive performance compared to state-of-the-art classical image registration methods.
This paper describes the application of a real coded genetic algorithm (GA) to align two or more 2-D images by means of image registration. The proposed search strategy is a transformation parameters-based approach involving the affine transform. The real coded GA uses Simulated Binary Crossover (SBX), a parent-centric recombination operator that has shown to deliver a good performance in many optimization problems in the continuous domain. In addition, we propose a new technique for matching points between a warped and static images by using a randomized ordering when visiting the points during the matching procedure. This new technique makes the evaluation of the objective function somewhat noisy, but GAs and other population-based search algorithms have been shown to cope well with noisy fitness evaluations. The results obtained are competitive to those obtained by state-of-the-art classical methods in image registration, confirming the usefulness of the proposed noisy objective function and the suitability of SBX as a recombination operator for this type of problem.