Please use this identifier to cite or link to this item: https://hdl.handle.net/10953/2321
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dc.contributor.authorSantamaria, J.-
dc.contributor.authorRivero-Cejudo, M.L.-
dc.date.accessioned2024-02-11T07:49:17Z-
dc.date.available2024-02-11T07:49:17Z-
dc.date.issued2022-02-03-
dc.identifier.citationSantamaría J., Rivero-Cejudo M.L.;. User­ Vs. Self-tuning Optimization: A Case Study on Image Registration. Applied Soft Computing: Techniques and Applications. Eds. Samarjeet Borah and Ranjit Panigrahi. Apple Academic Press – CRC Press. ISBN-978-1-77463-029-7, 2022. https://doi.org/10.1201/9781003186885es_ES
dc.identifier.isbn9781003186885es_ES
dc.identifier.urihttps://doi.org/10.1201/9781003186885es_ES
dc.identifier.urihttps://hdl.handle.net/10953/2321-
dc.descriptionEl trabajo corresponde a una publicación con invitación realizada por los editores del libro, en cuya elaboración participaron miembros del equipo de investigación PAIDI de la Universidad de Jaén "Ingeniería Computacional Aplicada", cuyo responsable es el primer autor de la publicación. La investigación corresponde a una línea de investigación desarrollada por el autor desde 2003.es_ES
dc.description.abstractIn the last few years, there has been an increased interest in providing new soft computing (SC) algorithms, e.g., evolutionary algorithms (EAs), in which it is not needed the tuning of the control parameters. Usually, this new approach is named self-tuned and several models have been proposed to date. In our study, we aim to, firstly, describe how self-tuning EAs work and, secondly, provide a computational comparison of algorithms dealing with a specific computer vision (CV) task, named image registration (IR), as baseline. In particular, the development of automated IR methods is a well-known issue within the CS field and it was largely addressed from multiple viewpoints. IR has been applied to a high number of real-world scenarios ranging from remote sensing to medical imaging, artificial vision, and computer-aided design. Then, in this contribution it is provided a comprehensive analysis of the self-tuning approach by means of the experimental comparison of several EA-based IR algorithms proposed in the state-of-the-art.es_ES
dc.language.isoenges_ES
dc.publisherTaylor & Francis Group. Apple Academic Presses_ES
dc.subjectComputer visiones_ES
dc.subjectImage registrationes_ES
dc.subjectSoftcomputinges_ES
dc.titleUser­ Vs. Self-tuning Optimization: A Case Study on Image Registrationes_ES
dc.typeinfo:eu-repo/semantics/bookes_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
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