Using a genetic algorithm to register an uncalibrated image pair to a 3D surface model

In this paper we present a successful application of genetic algorithms to the registration of uncalibrated optical images to a 3D surface model. The problem is to find the projection matrices corresponding to the images in order to project the texture on the surface as precisely as possible. Recently, we have proposed a novel method that generalises the photo-consistency approach by Clarkson et al. to the case of uncalibrated cameras by using a genetic algorithm. In previous studies we focus on the computer vision aspects of the method, while here we analyse the genetic part. In particular, we use synthetic data to study the performance of different GAs and various types of selector, mutation and crossover. New experimental results on real data are also presented to demonstrate the efficiency of the method.