title: A Multiobjective Approach to Homography Estimation.
abstract: In several machine vision problems, a relevant issue is the estimation of homographies between two different perspectives that hold an extensive set of abnormal data. A method to find such estimation is the random sampling consensus (RANSAC); in this, the goal is to maximize the number of matching points given a permissible error (Pe), according to a candidate model. However, those objectives are in conflict: a low Pe value increases the accuracy of the model but degrades its generalization ability that refers to the number of matching points that tolerate noisy data, whereas a high Pe value improves the noise tolerance of the model but adversely drives the process to false detections. This work considers the estimation process as a multiobjective optimization problem that seeks to maximize the number of matching points whereas Pe is simultaneously minimized. In order to solve the multiobjective formulation, two different evolutionary algorithms have been explored: the Nondominated Sorting Genetic Algorithm II (NSGA-II) and the Nondominated Sorting Differential Evolution (NSDE). Results considering acknowledged quality measures among original and transformed images over a well-known image benchmark show superior performance of the proposal than Random Sample Consensus algorithm.
authors.affiliation: Sciences Division, Centro Universitario de Tonalá of Universidad de Guadalajara, 45400 Guadalajara, JAL, Mexico.
authors.forename: Valentín
authors.initials: V
authors.lastname: Osuna-Enciso
authors.affiliation: Electronic Division, Centro Universitario de Ciencias Exactas e Ingenierías of Universidad de Guadalajara, 44430 Guadalajara, JAL, Mexico.
authors.forename: Erik
authors.identifier: 0000-0002-0358-6049
authors.initials: E
authors.lastname: Cuevas
authors.affiliation: Electronic Division, Centro Universitario de Ciencias Exactas e Ingenierías of Universidad de Guadalajara, 44430 Guadalajara, JAL, Mexico; Computer Sciences Department, Tecnológico de Monterrey Campus Guadalajara, 45201 Guadalajara, JAL, Mexico.
authors.forename: Diego
authors.initials: D
authors.lastname: Oliva
authors.affiliation: Sciences Division, Centro Universitario de Tonalá of Universidad de Guadalajara, 45400 Guadalajara, JAL, Mexico.
authors.forename: Virgilio
authors.initials: V
authors.lastname: Zúñiga
authors.affiliation: Sciences Division, Centro Universitario de Tonalá of Universidad de Guadalajara, 45400 Guadalajara, JAL, Mexico.
authors.forename: Marco
authors.initials: M
authors.lastname: Pérez-Cisneros
authors.affiliation: Electronic Division, Centro Universitario de Ciencias Exactas e Ingenierías of Universidad de Guadalajara, 44430 Guadalajara, JAL, Mexico.
authors.forename: Daniel
authors.initials: D
authors.lastname: Zaldívar
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003661:Decision Support Techniques; D006801:Humans; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated / Q000379:methods*
references.citation: IEEE Trans Pattern Anal Mach Intell. 2004 Nov;26(11):1459-74
references.pmid: 15521494
references.citation: IEEE Trans Cybern. 2013 Apr;43(2):445-63
references.pmid: 22907971
references.citation: IEEE Trans Cybern. 2013 Jun;43(3):1088-101
references.pmid: 23193246
references.citation: IEEE Trans Cybern. 2013 Dec;43(6):1656-71
references.pmid: 24273143
references.citation: Comput Intell Neurosci. 2015;2015:434263
references.pmid: 26339228
references.citation: IEEE Trans Cybern. 2014 Dec;44(12):2568-84
references.pmid: 24718582
references.citation: IEEE Trans Cybern. 2014 Dec;44(12):2274-87
references.pmid: 25296410
references.citation: IEEE Trans Cybern. 2014 Dec;44(12):2771-83
references.pmid: 24771605