Neuro-inspired edge feature fusion using Choquet integrals

It is known that the human visual system performs a hierarchical information process in which early vision cues (or primitives) are fused in the visual cortex to compose complex shapes and descriptors. While different aspects of the process have been extensively studied, such as lens adaptation or f...

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Detalles Bibliográficos
Autores: Marco Detchart, Cedric, Lucca, Giancarlo, López Molina, Carlos, Miguel Turullols, Laura de, Pereira Dimuro, Graçaliz, Bustince Sola, Humberto
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Institución:Universidad Pública de Navarra
Repositorio:Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
OAI Identifier:oai:academica-e.unavarra.es:2454/42714
Acceso en línea:https://hdl.handle.net/2454/42714
Access Level:acceso abierto
Palabra clave:CF-integral
Choquet integral
Edge detection
Feature extraction
Image processing
Re-aggregation functions
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spelling Neuro-inspired edge feature fusion using Choquet integralsMarco Detchart, CedricLucca, GiancarloLópez Molina, CarlosMiguel Turullols, Laura dePereira Dimuro, GraçalizBustince Sola, HumbertoCF-integralChoquet integralEdge detectionFeature extractionImage processingRe-aggregation functionsIt is known that the human visual system performs a hierarchical information process in which early vision cues (or primitives) are fused in the visual cortex to compose complex shapes and descriptors. While different aspects of the process have been extensively studied, such as lens adaptation or feature detection, some other aspects, such as feature fusion, have been mostly left aside. In this work, we elaborate on the fusion of early vision primitives using generalizations of the Choquet integral, and novel aggregation operators that have been extensively studied in recent years. We propose to use generalizations of the Choquet integral to sensibly fuse elementary edge cues, in an attempt to model the behaviour of neurons in the early visual cortex. Our proposal leads to a fully-framed edge detection algorithm whose performance is put to the test in state-of-the-art edge detection datasets.The authors gratefully acknowledge the financial support of the Spanish Ministry of Science and Technology (project PID2019-108392GB-I00 (AEI/10.13039/501100011033), the Research Services of Universidad Pública de Navarra, CNPq (307781/2016-0, 301618/2019-4), FAPERGS (19/2551-0001660) and PNPD/CAPES (464880/2019-00).ElsevierEstadística, Informática y MatemáticasEstatistika, Informatika eta MatematikaUniversidad Pública de Navarra / Nafarroako Unibertsitate Publikoa2021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/2454/42714reponame:Academica-e. Repositorio Institucional de la Universidad Pública de Navarrainstname:Universidad Pública de NavarraInglésinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-108392GB-I00© 2021 The Authors. Creative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:academica-e.unavarra.es:2454/427142026-06-17T12:41:47Z
dc.title.none.fl_str_mv Neuro-inspired edge feature fusion using Choquet integrals
title Neuro-inspired edge feature fusion using Choquet integrals
spellingShingle Neuro-inspired edge feature fusion using Choquet integrals
Marco Detchart, Cedric
CF-integral
Choquet integral
Edge detection
Feature extraction
Image processing
Re-aggregation functions
title_short Neuro-inspired edge feature fusion using Choquet integrals
title_full Neuro-inspired edge feature fusion using Choquet integrals
title_fullStr Neuro-inspired edge feature fusion using Choquet integrals
title_full_unstemmed Neuro-inspired edge feature fusion using Choquet integrals
title_sort Neuro-inspired edge feature fusion using Choquet integrals
dc.creator.none.fl_str_mv Marco Detchart, Cedric
Lucca, Giancarlo
López Molina, Carlos
Miguel Turullols, Laura de
Pereira Dimuro, Graçaliz
Bustince Sola, Humberto
author Marco Detchart, Cedric
author_facet Marco Detchart, Cedric
Lucca, Giancarlo
López Molina, Carlos
Miguel Turullols, Laura de
Pereira Dimuro, Graçaliz
Bustince Sola, Humberto
author_role author
author2 Lucca, Giancarlo
López Molina, Carlos
Miguel Turullols, Laura de
Pereira Dimuro, Graçaliz
Bustince Sola, Humberto
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Estadística, Informática y Matemáticas
Estatistika, Informatika eta Matematika
Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
dc.subject.none.fl_str_mv CF-integral
Choquet integral
Edge detection
Feature extraction
Image processing
Re-aggregation functions
topic CF-integral
Choquet integral
Edge detection
Feature extraction
Image processing
Re-aggregation functions
description It is known that the human visual system performs a hierarchical information process in which early vision cues (or primitives) are fused in the visual cortex to compose complex shapes and descriptors. While different aspects of the process have been extensively studied, such as lens adaptation or feature detection, some other aspects, such as feature fusion, have been mostly left aside. In this work, we elaborate on the fusion of early vision primitives using generalizations of the Choquet integral, and novel aggregation operators that have been extensively studied in recent years. We propose to use generalizations of the Choquet integral to sensibly fuse elementary edge cues, in an attempt to model the behaviour of neurons in the early visual cortex. Our proposal leads to a fully-framed edge detection algorithm whose performance is put to the test in state-of-the-art edge detection datasets.
publishDate 2021
dc.date.none.fl_str_mv 2021
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/2454/42714
url https://hdl.handle.net/2454/42714
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-108392GB-I00
dc.rights.none.fl_str_mv © 2021 The Authors. Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv © 2021 The Authors. Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
instname:Universidad Pública de Navarra
instname_str Universidad Pública de Navarra
reponame_str Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
collection Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
repository.name.fl_str_mv
repository.mail.fl_str_mv
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