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A multiobjective approach to homography estimation

dc.creatorDiego Alberto Oliva Navarro
dc.date2016
dc.date.accessioned2018-10-18T15:34:05Z
dc.date.available2018-10-18T15:34:05Z
dc.identifier.issn16875265
dc.identifier.doi10.1155/2016/3629174
dc.identifier.urihttp://hdl.handle.net/11285/630262
dc.descriptionIn 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. © 2016 Valentín Osuna-Enciso et al.
dc.languageeng
dc.publisherHindawi Publishing Corporation
dc.relationhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84956912023&doi=10.1155%2f2016%2f3629174&partnerID=40&md5=7c4801a17f63a49eb49a3b1048534d3a
dc.relationInvestigadores
dc.relationEstudiantes
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0
dc.sourceComputational Intelligence and Neuroscience
dc.subjectBenchmarking
dc.subjectComputer vision
dc.subjectDigital storage
dc.subjectEvolutionary algorithms
dc.subjectGenetic algorithms
dc.subjectMultiobjective optimization
dc.subjectOptimization
dc.subjectSorting
dc.subjectDifferential Evolution
dc.subjectGeneralization ability
dc.subjectHomography estimations
dc.subjectMulti-objective formulation
dc.subjectMulti-objective optimization problem
dc.subjectMultiobjective approach
dc.subjectNon dominated sorting genetic algorithm ii (NSGA II)
dc.subjectRandom sample consensus
dc.subjectImage matching
dc.subjectalgorithm
dc.subjectartificial intelligence
dc.subjectautomated pattern recognition
dc.subjectcomputer simulation
dc.subjectdecision support system
dc.subjecthuman
dc.subjectprocedures
dc.subjecttheoretical model
dc.subjectAlgorithms
dc.subjectArtificial Intelligence
dc.subjectComputer Simulation
dc.subjectDecision Support Techniques
dc.subjectHumans
dc.subjectModels, Theoretical
dc.subjectPattern Recognition, Automated
dc.subject.classification7 INGENIERÍA Y TECNOLOGÍA
dc.titleA multiobjective approach to homography estimation
dc.typeArtículo
dc.identifier.volume2016
refterms.dateFOA2018-10-18T15:34:05Z


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