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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPEW34M/45CS5L8
Repositorysid.inpe.br/sibgrapi/2021/09.06.07.13
Last Update2021:09.06.20.38.31 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2021/09.06.07.13.28
Metadata Last Update2022:06.14.00.00.27 (UTC) administrator
DOI10.1109/SIBGRAPI54419.2021.00055
Citation KeyFonsecaNegPerCouGui:2021:ToAuMa
TitleNew hierarchy-based segmentation layer: towards automatic marker proposal
FormatOn-line
Year2021
Access Date2024, May 06
Number of Files1
Size2213 KiB
2. Context
Author1 Fonseca, Gabriel Barbosa da
2 Negrel, Romain
3 Perret, Benjamin
4 Cousty, Jean
5 Guimarães, Silvio Jamil Ferzoli
Affiliation1 Pontifícia Universidade Católica de Minas Gerais  
2 ESIEE Paris  
3 LIGM, Université Gustave Eiffel, CNRS, ESIEE Paris  
4 LIGM, Université Gustave Eiffel, CNRS, ESIEE Paris  
5 Pontifícia Universidade Católica de Minas Gerais
EditorPaiva, Afonso
Menotti, David
Baranoski, Gladimir V. G.
Proença, Hugo Pedro
Junior, Antonio Lopes Apolinario
Papa, João Paulo
Pagliosa, Paulo
dos Santos, Thiago Oliveira
e Sá, Asla Medeiros
da Silveira, Thiago Lopes Trugillo
Brazil, Emilio Vital
Ponti, Moacir A.
Fernandes, Leandro A. F.
Avila, Sandra
e-Mail Addressgbrl12@gmail.com
Conference NameConference on Graphics, Patterns and Images, 34 (SIBGRAPI)
Conference LocationGramado, RS, Brazil (virtual)
Date18-22 Oct. 2021
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2021-09-06 20:38:32 :: gbrl12@gmail.com -> administrator :: 2021
2022-03-02 00:54:15 :: administrator -> menottid@gmail.com :: 2021
2022-03-02 13:25:45 :: menottid@gmail.com -> administrator :: 2021
2022-06-14 00:00:27 :: administrator -> :: 2021
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordsinteractive image segmentation
automatic marker proposal
segmentation layer
deep learning
AbstractImage segmentation is an ill-posed problem by definition, as it is not always possible to automatically select which object appearing in an image is the object of interest. To deal with this issue, prior knowledge in the form of human-given markers can be included in the segmentation pipeline. Even though user interaction can drastically improve segmentation results, it is an expensive resource, and finding ways to reduce human effort on an interactive segmentation loop is of great interest. In this work, we propose a new segmentation layer to be used with deep neural networks, which allows us to create and train in an end-to-end fashion a marker creation network. To train the network, we propose a loss function composed of: a segmentation loss using the proposed differentiable segmentation layer; and a set of regularization functions that enforce the desired characteristics on the produced markers. We showed that by using the proposed layer and loss function, we can train the network to automatically generate markers that recover a good segmentation and have desirable shape characteristics. This behavior is observed on the training dataset, as well as on four unseen datasets.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2021 > New hierarchy-based segmentation...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > New hierarchy-based segmentation...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/45CS5L8
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/45CS5L8
Languageen
Target FileSIBGRAPI2021_learning_markers_CR2.pdf
User Groupgbrl12@gmail.com
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/45PQ3RS
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2021/11.12.11.46 8
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume


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