Fully parallel homological region adjacency graph via frontier recognition

Relating image contours and regions and their attributes according to connectivity based on incidence or adjacency is a crucial task in numerous applications in the fields of image processing, computer vision and pattern recognition. In this paper, the crucial incidence topological information of 2-...

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Autores: Díaz del Río, Fernando, Sánchez Cuevas, Pablo, Morón Fernández, María José, Cascado Caballero, Daniel, Molina Abril, Helena, Real Jurado, Pedro
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/159079
Acceso en línea:https://hdl.handle.net/11441/159079
https://doi.org/10.3390/a16060284
Access Level:acceso abierto
Palabra clave:Digital image
Parallel computing
Abstract cell complex
Region adjacency graph
Dual graph
Euler number
Homological information
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spelling Fully parallel homological region adjacency graph via frontier recognitionDíaz del Río, FernandoSánchez Cuevas, PabloMorón Fernández, María JoséCascado Caballero, DanielMolina Abril, HelenaReal Jurado, PedroDigital imageParallel computingAbstract cell complexRegion adjacency graphDual graphEuler numberHomological informationRelating image contours and regions and their attributes according to connectivity based on incidence or adjacency is a crucial task in numerous applications in the fields of image processing, computer vision and pattern recognition. In this paper, the crucial incidence topological information of 2-dimensional images is extracted in an efficient manner through the computation of a new structure called the HomDuRAG of an image; that is, the dual graph of the HomRAG (a topologically consistent extended version of the classical RAG). These representations are derived from the two traditional self-dual square grids (in which physical pixels play the role of 2-dimensional cells) and encapsulate the whole set of topological features and relations between the three types of objects embedded in a digital image: 2-dimensional (regions), 1-dimensional (contours) and 0-dimensional objects (crosses). Here, a first version of a fully parallel algorithm to compute this new representation is presented, whose timing complexity order (in the worst case and supposing one processing element per 0-cell) isMDPIMatemática Aplicada IArquitectura y Tecnología de ComputadoresTEP108: Robótica y Tecnología de ComputadoresTIC245: Topological Pattern Analysis, Recognition and LearningMinisterio de Economia, Industria y Competitividad (MINECO). EspañaAgencia Estatal de Investigación. EspañaEuropean Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER)2023info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/159079https://doi.org/10.3390/a16060284reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésAlgorithms, 16(6) (284).PID2019-110455GB-I00MCIN/AEI/10.13039/501100011033CIUCAP-HSFUS-1381077TED2021-130825B-I00https://www.mdpi.com/1999-4893/16/6/284info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1590792026-06-17T12:51:07Z
dc.title.none.fl_str_mv Fully parallel homological region adjacency graph via frontier recognition
title Fully parallel homological region adjacency graph via frontier recognition
spellingShingle Fully parallel homological region adjacency graph via frontier recognition
Díaz del Río, Fernando
Digital image
Parallel computing
Abstract cell complex
Region adjacency graph
Dual graph
Euler number
Homological information
title_short Fully parallel homological region adjacency graph via frontier recognition
title_full Fully parallel homological region adjacency graph via frontier recognition
title_fullStr Fully parallel homological region adjacency graph via frontier recognition
title_full_unstemmed Fully parallel homological region adjacency graph via frontier recognition
title_sort Fully parallel homological region adjacency graph via frontier recognition
dc.creator.none.fl_str_mv Díaz del Río, Fernando
Sánchez Cuevas, Pablo
Morón Fernández, María José
Cascado Caballero, Daniel
Molina Abril, Helena
Real Jurado, Pedro
author Díaz del Río, Fernando
author_facet Díaz del Río, Fernando
Sánchez Cuevas, Pablo
Morón Fernández, María José
Cascado Caballero, Daniel
Molina Abril, Helena
Real Jurado, Pedro
author_role author
author2 Sánchez Cuevas, Pablo
Morón Fernández, María José
Cascado Caballero, Daniel
Molina Abril, Helena
Real Jurado, Pedro
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Matemática Aplicada I
Arquitectura y Tecnología de Computadores
TEP108: Robótica y Tecnología de Computadores
TIC245: Topological Pattern Analysis, Recognition and Learning
Ministerio de Economia, Industria y Competitividad (MINECO). España
Agencia Estatal de Investigación. España
European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER)
dc.subject.none.fl_str_mv Digital image
Parallel computing
Abstract cell complex
Region adjacency graph
Dual graph
Euler number
Homological information
topic Digital image
Parallel computing
Abstract cell complex
Region adjacency graph
Dual graph
Euler number
Homological information
description Relating image contours and regions and their attributes according to connectivity based on incidence or adjacency is a crucial task in numerous applications in the fields of image processing, computer vision and pattern recognition. In this paper, the crucial incidence topological information of 2-dimensional images is extracted in an efficient manner through the computation of a new structure called the HomDuRAG of an image; that is, the dual graph of the HomRAG (a topologically consistent extended version of the classical RAG). These representations are derived from the two traditional self-dual square grids (in which physical pixels play the role of 2-dimensional cells) and encapsulate the whole set of topological features and relations between the three types of objects embedded in a digital image: 2-dimensional (regions), 1-dimensional (contours) and 0-dimensional objects (crosses). Here, a first version of a fully parallel algorithm to compute this new representation is presented, whose timing complexity order (in the worst case and supposing one processing element per 0-cell) is
publishDate 2023
dc.date.none.fl_str_mv 2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/159079
https://doi.org/10.3390/a16060284
url https://hdl.handle.net/11441/159079
https://doi.org/10.3390/a16060284
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Algorithms, 16(6) (284).
PID2019-110455GB-I00
MCIN/AEI/10.13039/501100011033
CIUCAP-HSF
US-1381077
TED2021-130825B-I00
https://www.mdpi.com/1999-4893/16/6/284
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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