Show simple item record

dc.creatorFumanal Idocin, Javieres_ES
dc.creatorUriarte Barragán, Juanes_ES
dc.creatorOsa Hernández, Borja de laes_ES
dc.creatorBardozzo, Francescoes_ES
dc.creatorFernández Fernández, Francisco Javieres_ES
dc.creatorBustince Sola, Humbertoes_ES
dc.date.accessioned2022-03-10T08:00:00Z
dc.date.available2022-03-10T08:00:00Z
dc.date.issued2021
dc.identifier.issn1613-0073
dc.identifier.urihttps://hdl.handle.net/2454/42489
dc.description.abstractImage thresholding is one of the most popular problems in image processing. However, changes inlightning and contrast in an image can cause trouble for the existing algorithms that use a global threshold for all the image. A solution for this problem is the adaptive thresholding, in which an image canhave different thresholds for different parts of the image. Yet, the problem of choosing the most suitable threshold for each region of the image is still open. In this paper we present the Gated Local Adaptive Binarization algorithm, in which we choose the most appropriate threshold for each region of the image using a logistic regression. Our results show that this algorithm can effectively learn the most appropriate threshold in each situation, and beats other adaptive binarization solutions for a standard dataset in the literature.en
dc.format.extent7 p.
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.publisherCEUR Workshop Proceedings (CEUR-WS.org)en
dc.relation.ispartofWILF’21: The 13th International Workshop on Fuzzy Logic and Applications, Dec. 20–22, 2021, Vietri sul Mare, Italyen
dc.rights© 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectFuzzy logicen
dc.subjectImage thresholdingen
dc.subjectImage processingen
dc.subjectAggregation functionsen
dc.titleGated local adaptive binarization using supervised learningen
dc.typeinfo:eu-repo/semantics/conferenceObjecten
dc.typeContribución a congreso / Biltzarrerako ekarpenaes
dc.contributor.departmentEstadística, Informática y Matemáticases_ES
dc.contributor.departmentEstatistika, Informatika eta Matematikaeu
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen
dc.rights.accessRightsAcceso abierto / Sarbide irekiaes
dc.type.versioninfo:eu-repo/semantics/publishedVersionen
dc.type.versionVersión publicada / Argitaratu den bertsioaes


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

© 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
Except where otherwise noted, this item's license is described as © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

El Repositorio ha recibido la ayuda de la Fundación Española para la Ciencia y la Tecnología para la realización de actividades en el ámbito del fomento de la investigación científica de excelencia, en la Línea 2. Repositorios institucionales (convocatoria 2020-2021).
Logo MinisterioLogo Fecyt