Research Article A Method for Estimating View Transformations from Image Correspondences Based on the Harmony Search Algorithm Erik Cuevas 1 and Margarita Díaz 2 1 Departamento de Ciencias Computacionales, Universidad de Guadalajara, CUCEI , Avenida Revoluci´ on 1500, 44430 Guadalajara, JAL, Mexico 2 Divisi´ on de Ciencia y Tecnolog´ ıa, Universidad de Guadalajara, CU-Norte, Carretera Federal No. 23, Km. 191, 46200 Colotl´ an, JAL, Mexico Correspondence should be addressed to Erik Cuevas; erik.cuevas@cucei.udg.mx Received 30 September 2014; Accepted 12 December 2014 Academic Editor: Rahib H. Abiyev Copyright © 2015 E. Cuevas and M. D´ ıaz. his is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In this paper, a new method for robustly estimating multiple view relations from point correspondences is presented. he approach combines the popular random sampling consensus (RANSAC) algorithm and the evolutionary method harmony search (HS). With this combination, the proposed method adopts a diferent sampling strategy than RANSAC to generate putative solutions. Under the new mechanism, at each iteration, new candidate solutions are built taking into account the quality of the models generated by previous candidate solutions, rather than purely random as it is the case of RANSAC. he rules for the generation of candidate solutions (samples) are motivated by the improvisation process that occurs when a musician searches for a better state of harmony. As a result, the proposed approach can substantially reduce the number of iterations still preserving the robust capabilities of RANSAC. he method is generic and its use is illustrated by the estimation of homographies, considering synthetic and real images. Additionally, in order to demonstrate the performance of the proposed approach within a real engineering application, it is employed to solve the problem of position estimation in a humanoid robot. Experimental results validate the eiciency of the proposed method in terms of accuracy, speed, and robustness. 1. Introduction he goal of estimating geometric relations in images is to ind an appropriate global transformation to overlay images of the same scene taken at diferent viewpoints. It can be applied in image processing when an object moves in front of a static camera and when a static scene is captured by a moving camera or multiple cameras from diferent viewpoints. his methodology has been widely adopted in many applications, for instance, when series of images can be stitched together to generate a panorama image [1–3]. Also, multiple image superresolution approaches can be applied in the overlapped region calculated according to the estimated geometry [4– 6]. he motion of a moving object can also be estimated using its geometric relations [7] and a distributed camera network can be calibrated, where each camera’s position, orientation, and focal length can be calculated based on their correspondences [8–10]. Another example is the robot position that can be controlled or estimated through the estimation of the fundamental matrix/homography [11–13]. In a modelling problem, those data that can be explained by the hypothetical model are known as inliers of this model. Other points, for example, those generated by matching errors, are called outliers. he outliers are caused by external efects not related to the investigated model. Based on dif- ferent criteria, several robust techniques have been proposed to identify points as inliers or outliers, being the random sampling consensus (RANSAC) algorithm [14] the most well known [15–17]. RANSAC adopts a simple hypothesize-and-evaluation process. Under such approach, a minimal subset of elements (correspondences) is sampled randomly, and a candidate model is hypothesized using this subset. hen, the candidate model is evaluated on the entire dataset separating all Hindawi Publishing Corporation Computational Intelligence and Neuroscience Volume 2015, Article ID 434263, 15 pages http://dx.doi.org/10.1155/2015/434263