A scatter search based optimizer for the registration of 3D surfaces

Image registration is a crucial task in many areas such as computer vision, neurosurgery, remote sensing, and cartography. Different approaches to solve the problem have been proposed in the specialized literature but present some drawbacks. In this contribution, we focus our interest on the 3D surface IR problem and our proposal considers the use of an emergent evolutionary global optimization strategy called scatter search, providing a fast and accurate algorithm to estimate similarity transformations. To measure its performance, we make a broad comparison with some of the most accepted and accurate classical and evolutionary techniques. The new proposal will be validated using two different surfaces, a synthetic and a magnetic resonance image, considered by other researchers in the field, both in noise-free and noisy scenarios.