Affine image registration using genetic algorithms and evolutionary strategies - Bazargani, Mosab (2018)
This thesis investigates the application of evolutionary algorithms 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. A noisy objective function is proposed and tested using two well-known evolutionary algorithms (EAs), the genetic algorithm (GA) as well as the evolutionary strategies (ES) that are suitable for this particular ill-posed problem. In contrast with GA, which was originally designed to work on binary representation, ES was originally developed to work in continuous search spaces. Surprisingly, results of the proposed real coded genetic algorithm are far superior when compared to results obtained from evolutionary strategies’ framework for the problem at hand. 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, 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, is proposed. 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 from GA formulation are competitive to those obtained by the 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.