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Computational Intelligence and Neuroscience
Volume 2015, Article ID 434263, 15 pages
Research Article

A Method for Estimating View Transformations from Image Correspondences Based on the Harmony Search Algorithm

1Departamento de Ciencias Computacionales, Universidad de Guadalajara, CUCEI , Avenida Revolución 1500, 44430 Guadalajara, JAL, Mexico
2División de Ciencia y Tecnología, Universidad de Guadalajara, CU-Norte, Carretera Federal No. 23, Km. 191, 46200 Colotlán, JAL, Mexico

Received 30 September 2014; Accepted 12 December 2014

Academic Editor: Rahib H. Abiyev

Copyright © 2015 Erik Cuevas and Margarita Díaz. This 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. The approach combines the popular random sampling consensus (RANSAC) algorithm and the evolutionary method harmony search (HS). With this combination, the proposed method adopts a different 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. The 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. The 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 efficiency of the proposed method in terms of accuracy, speed, and robustness.