A study of observation scales based on felzenswalb-huttenlocher dissimilarity measure for hierarchical segmentation
Descripción del Articulo
The research leading to these results has received funding from the French Agence Nationale de la Recherche, grant ANR-15-CE40-0006 (CoMeDiC), the Brazilian Federal Agency of Support and Evaluation of Postgraduate Education (program CAPES/PVE: grant 064965/2014-01), Brazilian Federal Agency of Resea...
| Autores: | , , , , , |
|---|---|
| Formato: | objeto de conferencia |
| Fecha de Publicación: | 2019 |
| Institución: | Consejo Nacional de Ciencia Tecnología e Innovación |
| Repositorio: | CONCYTEC-Institucional |
| Lenguaje: | inglés |
| OAI Identifier: | oai:repositorio.concytec.gob.pe:20.500.12390/503 |
| Enlace del recurso: | https://hdl.handle.net/20.500.12390/503 https://doi.org/10.1007/978-3-030-14085-4_14 |
| Nivel de acceso: | acceso abierto |
| Materia: | Segmentation results Geometry Graphic methods Dissimilarity measures Hierarchical graphs Hierarchical segmentation Scale selection Scale spaces Image segmentation https://purl.org/pe-repo/ocde/ford#2.02.00 |
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| dc.title.none.fl_str_mv |
A study of observation scales based on felzenswalb-huttenlocher dissimilarity measure for hierarchical segmentation |
| title |
A study of observation scales based on felzenswalb-huttenlocher dissimilarity measure for hierarchical segmentation |
| spellingShingle |
A study of observation scales based on felzenswalb-huttenlocher dissimilarity measure for hierarchical segmentation Cayllahua-Cahuina E. Segmentation results Geometry Graphic methods Dissimilarity measures Hierarchical graphs Hierarchical segmentation Scale selection Scale spaces Image segmentation Image segmentation https://purl.org/pe-repo/ocde/ford#2.02.00 |
| title_short |
A study of observation scales based on felzenswalb-huttenlocher dissimilarity measure for hierarchical segmentation |
| title_full |
A study of observation scales based on felzenswalb-huttenlocher dissimilarity measure for hierarchical segmentation |
| title_fullStr |
A study of observation scales based on felzenswalb-huttenlocher dissimilarity measure for hierarchical segmentation |
| title_full_unstemmed |
A study of observation scales based on felzenswalb-huttenlocher dissimilarity measure for hierarchical segmentation |
| title_sort |
A study of observation scales based on felzenswalb-huttenlocher dissimilarity measure for hierarchical segmentation |
| author |
Cayllahua-Cahuina E. |
| author_facet |
Cayllahua-Cahuina E. Cousty J. Guimarães S. Kenmochi Y. Cámara-Chávez G. de Albuquerque Araújo A. |
| author_role |
author |
| author2 |
Cousty J. Guimarães S. Kenmochi Y. Cámara-Chávez G. de Albuquerque Araújo A. |
| author2_role |
author author author author author |
| dc.contributor.author.fl_str_mv |
Cayllahua-Cahuina E. Cousty J. Guimarães S. Kenmochi Y. Cámara-Chávez G. de Albuquerque Araújo A. |
| dc.subject.none.fl_str_mv |
Segmentation results |
| topic |
Segmentation results Geometry Graphic methods Dissimilarity measures Hierarchical graphs Hierarchical segmentation Scale selection Scale spaces Image segmentation Image segmentation https://purl.org/pe-repo/ocde/ford#2.02.00 |
| dc.subject.es_PE.fl_str_mv |
Geometry Graphic methods Dissimilarity measures Hierarchical graphs Hierarchical segmentation Scale selection Scale spaces Image segmentation Image segmentation |
| dc.subject.ocde.none.fl_str_mv |
https://purl.org/pe-repo/ocde/ford#2.02.00 |
| description |
The research leading to these results has received funding from the French Agence Nationale de la Recherche, grant ANR-15-CE40-0006 (CoMeDiC), the Brazilian Federal Agency of Support and Evaluation of Postgraduate Education (program CAPES/PVE: grant 064965/2014-01), Brazilian Federal Agency of Research (CNPq/Universal 421521/2016-3 and CNPq/PQ 307062/2016-3), Fundo de Amparo Pesquisa do Estado de Minas Gerais (FAPEMIG/PPM 00006-16), the Peruvian agency Consejo Nacional de Ciencia, Tecnológica CONCYTEC (contract N 101-2016-. FONDECYT-DE). The first author would like to thank Brazilian agencies CNPq and CAPES and Peruvian agency CONCYTEC for the financial support during his thesis. |
| publishDate |
2019 |
| dc.date.accessioned.none.fl_str_mv |
2024-05-30T23:13:38Z |
| dc.date.available.none.fl_str_mv |
2024-05-30T23:13:38Z |
| dc.date.issued.fl_str_mv |
2019 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
| format |
conferenceObject |
| dc.identifier.isbn.none.fl_str_mv |
9783030140847 |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12390/503 |
| dc.identifier.doi.none.fl_str_mv |
https://doi.org/10.1007/978-3-030-14085-4_14 |
| dc.identifier.scopus.none.fl_str_mv |
2-s2.0-85064214726 |
| identifier_str_mv |
9783030140847 2-s2.0-85064214726 |
| url |
https://hdl.handle.net/20.500.12390/503 https://doi.org/10.1007/978-3-030-14085-4_14 |
| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
| dc.relation.ispartof.none.fl_str_mv |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess |
| eu_rights_str_mv |
openAccess |
| dc.publisher.none.fl_str_mv |
Springer Verlag |
| publisher.none.fl_str_mv |
Springer Verlag |
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reponame:CONCYTEC-Institucional instname:Consejo Nacional de Ciencia Tecnología e Innovación instacron:CONCYTEC |
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Consejo Nacional de Ciencia Tecnología e Innovación |
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CONCYTEC |
| institution |
CONCYTEC |
| reponame_str |
CONCYTEC-Institucional |
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CONCYTEC-Institucional |
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Repositorio Institucional CONCYTEC |
| repository.mail.fl_str_mv |
repositorio@concytec.gob.pe |
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1839175547191558144 |
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Publicationrp00649600rp00651600rp00653600rp00654600rp00652600rp00650600Cayllahua-Cahuina E.Cousty J.Guimarães S.Kenmochi Y.Cámara-Chávez G.de Albuquerque Araújo A.2024-05-30T23:13:38Z2024-05-30T23:13:38Z20199783030140847https://hdl.handle.net/20.500.12390/503https://doi.org/10.1007/978-3-030-14085-4_142-s2.0-85064214726The research leading to these results has received funding from the French Agence Nationale de la Recherche, grant ANR-15-CE40-0006 (CoMeDiC), the Brazilian Federal Agency of Support and Evaluation of Postgraduate Education (program CAPES/PVE: grant 064965/2014-01), Brazilian Federal Agency of Research (CNPq/Universal 421521/2016-3 and CNPq/PQ 307062/2016-3), Fundo de Amparo Pesquisa do Estado de Minas Gerais (FAPEMIG/PPM 00006-16), the Peruvian agency Consejo Nacional de Ciencia, Tecnológica CONCYTEC (contract N 101-2016-. FONDECYT-DE). The first author would like to thank Brazilian agencies CNPq and CAPES and Peruvian agency CONCYTEC for the financial support during his thesis.Hierarchical image segmentation provides a region-oriented scale-space, i.e., a set of image segmentations at different detail levels in which the segmentations at finer levels are nested with respect to those at coarser levels. Guimarães et al. proposed a hierarchical graph based image segmentation (HGB) method based on the Felzenszwalb-Huttenlocher dissimilarity. This HGB method computes, for each edge of a graph, the minimum scale in a hierarchy at which two regions linked by this edge should merge according to the dissimilarity. In order to generalize this method, we first propose an algorithm to compute the intervals which contain all the observation scales at which the associated regions should merge. Then, following the current trend in mathematical morphology to study criteria which are not increasing on a hierarchy, we present various strategies to select a significant observation scale in these intervals. We use the BSDS dataset to assess our observation scale selection methods. The experiments show that some of these strategies lead to better segmentation results than the ones obtained with the original HGB method.Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - ConcytecengSpringer VerlagLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)info:eu-repo/semantics/openAccessSegmentation resultsGeometry-1Graphic methods-1Dissimilarity measures-1Hierarchical graphs-1Hierarchical segmentation-1Scale selection-1Scale spaces-1Image segmentation-1Image segmentation-1https://purl.org/pe-repo/ocde/ford#2.02.00-1A study of observation scales based on felzenswalb-huttenlocher dissimilarity measure for hierarchical segmentationinfo:eu-repo/semantics/conferenceObjectreponame:CONCYTEC-Institucionalinstname:Consejo Nacional de Ciencia Tecnología e Innovacióninstacron:CONCYTEC#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#20.500.12390/503oai:repositorio.concytec.gob.pe:20.500.12390/5032024-05-30 15:22:01.071http://purl.org/coar/access_right/c_14cbinfo:eu-repo/semantics/closedAccessmetadata only accesshttps://repositorio.concytec.gob.peRepositorio Institucional CONCYTECrepositorio@concytec.gob.pe#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="579d9220-73de-4b23-a4e9-10e9cdf6ec4a"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>A study of observation scales based on felzenswalb-huttenlocher dissimilarity measure for hierarchical segmentation</Title> <PublishedIn> <Publication> <Title>Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</Title> </Publication> </PublishedIn> <PublicationDate>2019</PublicationDate> <DOI>https://doi.org/10.1007/978-3-030-14085-4_14</DOI> <SCP-Number>2-s2.0-85064214726</SCP-Number> <ISBN>9783030140847</ISBN> <Authors> <Author> <DisplayName>Cayllahua-Cahuina E.</DisplayName> <Person id="rp00649" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Cousty J.</DisplayName> <Person id="rp00651" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Guimarães S.</DisplayName> <Person id="rp00653" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Kenmochi Y.</DisplayName> <Person id="rp00654" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Cámara-Chávez G.</DisplayName> <Person id="rp00652" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>de Albuquerque Araújo A.</DisplayName> <Person id="rp00650" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Springer Verlag</DisplayName> <OrgUnit /> </Publisher> </Publishers> <Keyword>Segmentation results</Keyword> <Keyword>Geometry</Keyword> <Keyword>Graphic methods</Keyword> <Keyword>Dissimilarity measures</Keyword> <Keyword>Hierarchical graphs</Keyword> <Keyword>Hierarchical segmentation</Keyword> <Keyword>Scale selection</Keyword> <Keyword>Scale spaces</Keyword> <Keyword>Image segmentation</Keyword> <Keyword>Image segmentation</Keyword> <Abstract>Hierarchical image segmentation provides a region-oriented scale-space, i.e., a set of image segmentations at different detail levels in which the segmentations at finer levels are nested with respect to those at coarser levels. Guimarães et al. proposed a hierarchical graph based image segmentation (HGB) method based on the Felzenszwalb-Huttenlocher dissimilarity. This HGB method computes, for each edge of a graph, the minimum scale in a hierarchy at which two regions linked by this edge should merge according to the dissimilarity. In order to generalize this method, we first propose an algorithm to compute the intervals which contain all the observation scales at which the associated regions should merge. Then, following the current trend in mathematical morphology to study criteria which are not increasing on a hierarchy, we present various strategies to select a significant observation scale in these intervals. We use the BSDS dataset to assess our observation scale selection methods. The experiments show that some of these strategies lead to better segmentation results than the ones obtained with the original HGB method.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication> -1 |
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13.436549 |
Nota importante:
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).
