A comparative study on the application of advanced bacterial foraging models to image registration

• Image registration aims to find a transformation between two different images.
• Evolutionary computation uses computational models of evolutionary processes.
• Evolutionary computation has been widely applied in image registration.
• We propose several variants of the bacterial foraging optimization algorithm.
• We analyze the performance of the proposed variants using range and medical images.

New swarm intelligence approaches as the Bacterial Foraging Optimization Algorithm (BFOA) have recently awakened a growing interest in the evolutionary computation community. This fact is due to the promising results obtained by different variants of the latter optimization method in many real-world applications. In this work we aim to take a step ahead in the development of the discipline by introducing a large amount of BFOA variants resulting from the combination of some advanced design decisions applied on Dasgupta et al.’s self-adaptive version. Our goal is thus to achieve an improved understanding of the good properties that the BFOA approach has shown in previous contributions. We will perform an extensive experimental study considering a plethora of algorithmic variants to solve a real-world problem, image registration, a well-known and complex task in computer vision. In particular, more than fifty variants are proposed and tested tackling pair-wise image registration problem instances from two different domains, namely range image registration for 3D model reconstruction and 3D medical image registration. The reported results reveal that BFOA is a versatile approach able to provide very competitive outcomes to face challenging real-world image registration problems when compared to the state-of-the-art evolutionary approaches in the field.