Using an Adaptive Invasion-based Model for Fast Range Image Registration

This paper presents an adaptive model for automatically pair-wise registering range images. Given two images and set one as the model, the aim is to find the best possible spatial transformation of the second image causing 3D reconstruction of the original object. Registration is effected here by using a distributed Differential Evolution algorithm characterized by a migration model inspired by the phenomenon known as biological invasion, and by applying a parallel Grid Closest Point algorithm. The distributed algorithm is endowed with two adaptive updating schemes to set the mutation and the crossover parameters, whereas the subpopulation size is assumed to be set in advance and kept fixed throughout the evolution process. The adaptive procedure is tied to the migration and is guided by a performance measure between two consecutive migrations. Experimental results achieved by our approach show the capability of this adaptive method of picking up efficient transformations of images and are compared with those of a recently proposed evolutionary algorithm. This efficiency is evaluated in terms of both quality and robustness of the reconstructed 3D image, and of computational cost.