Please use this identifier to cite or link to this item:
http://hdl.handle.net/10553/46776
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Antón-Canalís, Luis | en_US |
dc.contributor.author | Hernández Tejera, Mario | en_US |
dc.contributor.author | Sánchez-Nielsen, Elena | en_US |
dc.date.accessioned | 2018-11-23T08:03:29Z | - |
dc.date.available | 2018-11-23T08:03:29Z | - |
dc.date.issued | 2012 | en_US |
dc.identifier.issn | 0031-3203 | en_US |
dc.identifier.uri | http://hdl.handle.net/10553/46776 | - |
dc.description.abstract | A 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.language | eng | en_US |
dc.relation | Tecnicas de Visión Para la Interacción en Entornos de Interior Con Elaboración Mapas Cognitivos en Sistemas Perceptuales Heterogéneos. | en_US |
dc.relation.ispartof | Pattern Recognition | en_US |
dc.source | Pattern Recognition [ISSN 0031-3203], v. 45 (9), p. 3125-3130 | en_US |
dc.subject | 1203 Ciencia de los ordenadores | en_US |
dc.subject | 120304 Inteligencia artificial | en_US |
dc.subject.other | Distance map | en_US |
dc.subject.other | Distance transform | en_US |
dc.subject.other | Pseudodistance | en_US |
dc.title | Distance maps from unthresholded magnitudes | en_US |
dc.type | info:eu-repo/semantics/article | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1016/j.patcog.2012.02.010 | en_US |
dc.identifier.scopus | 84861588756 | - |
dc.identifier.isi | 000306091900008 | - |
dc.contributor.authorscopusid | 8921191600 | - |
dc.contributor.authorscopusid | 55966875800 | - |
dc.contributor.authorscopusid | 13105159100 | - |
dc.identifier.eissn | 1873-5142 | - |
dc.description.lastpage | 3130 | en_US |
dc.identifier.issue | 9 | - |
dc.description.firstpage | 3125 | en_US |
dc.relation.volume | 45 | en_US |
dc.investigacion | Ingeniería y Arquitectura | en_US |
dc.type2 | Artículo | en_US |
dc.contributor.daisngid | 3547239 | - |
dc.contributor.daisngid | 2188888 | - |
dc.contributor.daisngid | 1518383 | - |
dc.utils.revision | Sí | en_US |
dc.contributor.wosstandard | WOS:Anton-Canalis, L | - |
dc.contributor.wosstandard | WOS:Hernandez-Tejera, M | - |
dc.contributor.wosstandard | WOS:Sanchez-Nielsen, E | - |
dc.date.coverdate | Septiembre 2012 | en_US |
dc.identifier.ulpgc | Sí | en_US |
dc.description.sjr | 1,376 | |
dc.description.jcr | 2,632 | |
dc.description.sjrq | Q1 | |
dc.description.jcrq | Q1 | |
dc.description.scie | SCIE | |
item.grantfulltext | none | - |
item.fulltext | Sin texto completo | - |
crisitem.author.dept | GIR SIANI: Inteligencia Artificial, Redes Neuronales, Aprendizaje Automático e Ingeniería de Datos | - |
crisitem.author.dept | IU Sistemas Inteligentes y Aplicaciones Numéricas | - |
crisitem.author.dept | Departamento de Informática y Sistemas | - |
crisitem.author.orcid | 0000-0001-9717-8048 | - |
crisitem.author.parentorg | IU Sistemas Inteligentes y Aplicaciones Numéricas | - |
crisitem.author.fullName | Hernández Tejera, Francisco Mario | - |
crisitem.project.principalinvestigator | Domínguez Brito, Antonio Carlos | - |
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