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dc.contributor.authorGálvez Tomida, Akemi 
dc.contributor.authorIglesias Prieto, Andrés 
dc.contributor.authorDíaz Severiano, José Andrés 
dc.contributor.authorFister, Iztok
dc.contributor.authorLópez Uriarte, Joaquin
dc.contributor.authorFister, Iztok Jr.
dc.contributor.otherUniversidad de Cantabriaes_ES
dc.date.accessioned2024-12-09T13:39:37Z
dc.date.available2024-12-09T13:39:37Z
dc.date.issued2021-01
dc.identifier.issn1474-0346
dc.identifier.issn1873-5320
dc.identifier.otherTIN2017-89275-Res_ES
dc.identifier.urihttps://hdl.handle.net/10902/34576
dc.description.abstractThis work is an extension of a previous paper (presented at the Cyberworlds 2019 conference) introducing a new method for fractal compression of bitmap binary images. That work is now extended and enhanced through three new valuable features: (1) the bat algorithm is replaced by an improved version based on optimal forage strategy (OFS) and random disturbance strategy (RDS); (2) the inclusion of new similarity metrics; and (3) the consideration of a variable number of contractive maps, whose value can change dynamically over the population and over the iterations. The first feature improves the search capability of the method, the second one improves the reconstruction accuracy, and the third one computes the optimal number of contractive maps automatically. This new scheme is applied to a benchmark of two binary fractal images exhibiting a complex and irregular fractal shape. The graphical and numerical results show that the method performs very well, being able to reconstruct the input images with high accuracy. It also computes the optimal number of contractive maps in a fully automatic way. A comparative work with other alternative methods described in the literature is also carried out. It shows that the presented method outperforms the previous approaches significantly.es_ES
dc.description.sponsorshipAndrés Iglesias and Akemi Gálvez have received funding from the project PDE-GIR of the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 778035, the Spanish Ministry of Science, Innovation and Universities (Computer Science National Program) under grant TIN2017–89275-R of the Agencia Estatal de Investigación and European Funds EFRD (AEI/FEDER, UE). Iztok Fister acknowledges financial support from the Slovenian Research Agency (Grant No. P2-0041). Iztok Fister Jr. acknowledges financial support from the Slovenian Research Agency (Grant No. P2-0057).es_ES
dc.format.extent35 p.es_ES
dc.language.isoenges_ES
dc.publisherElsevier Limitedes_ES
dc.rights© 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 licensees_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.sourceAdvanced Engineering Informatics, 2021, 47, 101222es_ES
dc.subject.otherSwarm intelligencees_ES
dc.subject.otherBat algorithmes_ES
dc.subject.otherFractal compressiones_ES
dc.subject.otherIterated function systemses_ES
dc.subject.otherBitmap imageses_ES
dc.titleModified OFS-RDS bat algorithm for IFS encoding of bitmap fractal binary imageses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherVersionhttps://doi.org/10.1016/j.aei.2020.101222es_ES
dc.rights.accessRightsopenAccesses_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/778035/EU/PDE-based geometric modelling, image processing, and shape reconstruction/PDE-GIR/es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-89275-R/ES/SWARM INTELLIGENCE PARA MODELADO Y RECONSTRUCCION DE FORMAS EN GRAFICOS POR COMPUTADOR, IMAGENES MEDICAS Y ROBOTICA/es_ES
dc.identifier.DOI10.1016/j.aei.2020.101222
dc.type.versionacceptedVersiones_ES


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© 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 licenseExcepto si se señala otra cosa, la licencia del ítem se describe como © 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license