dc.contributor.editor | Rosin, Paul | es |
dc.contributor.editor | Adamatzky, Andrew | es |
dc.contributor.editor | Sun, Xianfang | es |
dc.creator | Díaz Pernil, Daniel | es |
dc.creator | Peña Cantillana, Francisco | es |
dc.creator | Gutiérrez Naranjo, Miguel Ángel | es |
dc.date.accessioned | 2021-04-08T09:07:49Z | |
dc.date.available | 2021-04-08T09:07:49Z | |
dc.date.issued | 2014 | |
dc.identifier.citation | Díaz Pernil, D., Peña Cantillana, F., y Gutiérrez Naranjo, M.Á. (2014). Skeletonizing Digital Images with Cellular Automata. En P. Rosin, A. Adamatzky, X. Sun (Ed.), Cellular Automata in Image Processing and Geometry (pp. 47-63). Cham, Switzerland: Springer. | |
dc.identifier.isbn | 978-3-319-06430-7 | es |
dc.identifier.uri | https://hdl.handle.net/11441/106820 | |
dc.description.abstract | The skeletonization of an image consists of converting the initial image
into a more compact representation. In general, the skeleton preserves the basic
struc-ture and, in some sense, keeps the meaning. The most important features
concerning a shape are its topology (represented by connected components, holes,
etc.) and its geometry (elongated parts, ramifications, etc.), thus they must be
preserved. Skele-tonization is usually considered as a pre-processing step in pattern
recognition algo-rithms, but its study is also interesting by itself for the analysis of
line-based images such as texts, line drawings, human fingerprints classification or
cartography.
Since the introduction of the concept by Blum in 1962 under the name of medial
axis transform, many algorithms have been published in this topic and there are
many different approaches to the problem, among them the ones based on distance
transform of the shape and skeleton pruning based on branch analysis. In this chapter,
we focus on how the skeletonization of an image can be studied in the Cellular
Automata framework and, as a case study, we consider in detail the Guo and Hall
skeletonizing algorithm. | es |
dc.description.sponsorship | Ministerio de Economía y Competitividad TIN2012-37434 | es |
dc.format | application/pdf | es |
dc.format.extent | 17 | es |
dc.language.iso | eng | es |
dc.publisher | Springer | es |
dc.relation.ispartof | Cellular Automata in Image Processing and Geometry | es |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.title | Skeletonizing Digital Images with Cellular Automata | es |
dc.type | info:eu-repo/semantics/bookPart | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Matemática Aplicada I (ETSII) | es |
dc.relation.projectID | TIN2012-37434 | es |
dc.relation.publisherversion | https://link.springer.com/chapter/10.1007/978-3-319-06431-4_3 | es |
dc.identifier.doi | 10.1007/978-3-319-06431-4_3 | es |
dc.contributor.group | Universidad de Sevilla. TIC193: Computación Natural | es |
dc.contributor.group | Universidad de Sevilla. FQM296: Topología Computacional y Matemática Aplicada | es |
dc.publication.initialPage | 47 | es |
dc.publication.endPage | 63 | es |
dc.relation.publicationplace | Cham, Switzerland | es |
dc.identifier.sisius | 20724277 | es |
dc.contributor.funder | Ministerio de Economía y Competitividad (MINECO). España | es |