Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/46776
DC FieldValueLanguage
dc.contributor.authorAntón-Canalís, Luisen_US
dc.contributor.authorHernández Tejera, Marioen_US
dc.contributor.authorSánchez-Nielsen, Elenaen_US
dc.date.accessioned2018-11-23T08:03:29Z-
dc.date.available2018-11-23T08:03:29Z-
dc.date.issued2012en_US
dc.identifier.issn0031-3203en_US
dc.identifier.urihttp://hdl.handle.net/10553/46776-
dc.description.abstractA straightforward algorithm that computes distance maps from unthresholded magnitudes is presented, suitable for still images and video sequences. While results on binary images are similar to classic Euclidean Distance Transforms, the proposed approach does not require a binarization step. Thus, no thresholds are needed and no information is lost in intermediate classification stages. Experiments include the evaluation of spatial and temporal coherence of distance map values, showing better results in both measurements than those obtained with Sobel or Deriche gradients and classic chessboard distance transforms.en_US
dc.languageengen_US
dc.relationTecnicas de Visión Para la Interacción en Entornos de Interior Con Elaboración Mapas Cognitivos en Sistemas Perceptuales Heterogéneos.en_US
dc.relation.ispartofPattern Recognitionen_US
dc.sourcePattern Recognition [ISSN 0031-3203], v. 45 (9), p. 3125-3130en_US
dc.subject1203 Ciencia de los ordenadoresen_US
dc.subject120304 Inteligencia artificialen_US
dc.subject.otherDistance mapen_US
dc.subject.otherDistance transformen_US
dc.subject.otherPseudodistanceen_US
dc.titleDistance maps from unthresholded magnitudesen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.patcog.2012.02.010en_US
dc.identifier.scopus84861588756-
dc.identifier.isi000306091900008-
dc.contributor.authorscopusid8921191600-
dc.contributor.authorscopusid55966875800-
dc.contributor.authorscopusid13105159100-
dc.identifier.eissn1873-5142-
dc.description.lastpage3130en_US
dc.identifier.issue9-
dc.description.firstpage3125en_US
dc.relation.volume45en_US
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Artículoen_US
dc.contributor.daisngid3547239-
dc.contributor.daisngid2188888-
dc.contributor.daisngid1518383-
dc.utils.revisionen_US
dc.contributor.wosstandardWOS:Anton-Canalis, L-
dc.contributor.wosstandardWOS:Hernandez-Tejera, M-
dc.contributor.wosstandardWOS:Sanchez-Nielsen, E-
dc.date.coverdateSeptiembre 2012en_US
dc.identifier.ulpgcen_US
dc.description.sjr1,376
dc.description.jcr2,632
dc.description.sjrqQ1
dc.description.jcrqQ1
dc.description.scieSCIE
item.grantfulltextnone-
item.fulltextSin texto completo-
crisitem.author.deptGIR SIANI: Inteligencia Artificial, Redes Neuronales, Aprendizaje Automático e Ingeniería de Datos-
crisitem.author.deptIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.deptDepartamento de Informática y Sistemas-
crisitem.author.orcid0000-0001-9717-8048-
crisitem.author.parentorgIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.fullNameHernández Tejera, Francisco Mario-
crisitem.project.principalinvestigatorDomínguez Brito, Antonio Carlos-
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