Using an Adaptive Invasion–based Model for Fast Range Image Registration Ivanoe De Falco ICAR–CNR Via P. Castellino, 111 Naples, Italy ivanoe.defalco@na.icar.cnr.it Antonio Della Cioppa University of Salerno Via Ponte Don Melillo, 1 Fisciano (SA), Italy adellacioppa@unisa.it Domenico Maisto ICAR–CNR Via P. Castellino, 111 Naples, Italy domenico.maisto@na.icar.cnr.it Umberto Scafuri ICAR–CNR Via P. Castellino, 111 Naples, Italy umberto.scafuri@na.icar.cnr.it Ernesto Tarantino ICAR–CNR Via P. Castellino, 111 Naples, Italy ernesto.tarantino@na.icar.cnr.it ABSTRACT 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. Categories and Subject Descriptors I.2.8 [Artificial Intelligence]: Problem Solving, Control Methods, and Search—heuristic methods ; I.4.3 [Image Processing and Computer Vision]: Enhancement— Registration Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. GECCO’14, July 12–16, 2014, Vancouver, BC, Canada. Copyright 2014 ACM 978-1-4503-2662-9/14/07 ...$15.00. http://dx.doi.org/10.1145/2576768.2598340. Keywords Distributed Differential Evolution, adaptive control param- eter setting, range image registration. 1. INTRODUCTION Range Image Registration (RIR) is a crucial task in computer vision used for integrating information acquired under diverse viewing angles (multi–view analysis). Over the years, several multi–view RIR techniques have been developed [21, 29, 17, 30] to tackle many practical applications, such as 3D modeling ranging from medical imaging, remote sensing, digital archeology, restoration of historic buildings, virtual museum, artificial vision, reverse engineering and computer–aided design (CAD) [27]. Since a physical object cannot be completely scanned with a single image due to the limited field of view of a sensor, a set of images taken from different positions is required to supply the information needed to build the whole 3D model. To avoid manually producing such a model by means of error–prone CAD–based techniques, multiple images are acquired by using range scanners [2] and joined together by a registration algorithm. The registration strategy can differ according to whether all range views of the objects are registered at the same time (multi–view registration) or only a pair of adjacent range images is processed in every execution (pair–wise registration). This paper is focused on the pair–wise registration of range images. As a consequence, starting from two views, i.e., the model and the scene, the objective of our registration process consists in finding the best spatial transformation that, when applied to the scene, aligns it with the model in a common coordinate system. Image registration is usually formulated as an optimiza- tion problem solved by iterative procedures. Among these, examples are Iterative Closest Point (ICP) methods [28], centered on the point–to–point and point–to–plane corre- spondences. However, as a drawback, the majority of these iterative algorithms requires to provide either a rough or a near–optimal prealignment of the images to avoid being trapped in local optima. Unfortunately, an exhaustive ex- ploration of the search space of all the candidate solutions becomes impracticable in case of absence of constraints for 1095