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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3PJ5ECP
Repositorysid.inpe.br/sibgrapi/2017/09.04.19.38
Last Update2017:09.04.19.38.19 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2017/09.04.19.38.19
Metadata Last Update2022:05.18.22.18.24 (UTC) administrator
Citation KeySilvaMontHiraHira:2017:ImOpLe
TitleImage operator learning based on local features
FormatOn-line
Year2017
Access Date2024, May 02
Number of Files1
Size754 KiB
2. Context
Author1 Silva, Augusto César Monteiro
2 Montagner, Igor dos Santos
3 Hirata Jr, Roberto
4 Hirata, Nina Sumiko Tomita
Affiliation1 Institute of Mathematics and Statistics
2 Institute of Mathematics and Statistics
3 Institute of Mathematics and Statistics
4 Institute of Mathematics and Statistics
EditorTorchelsen, Rafael Piccin
Nascimento, Erickson Rangel do
Panozzo, Daniele
Liu, Zicheng
Farias, Mylène
Viera, Thales
Sacht, Leonardo
Ferreira, Nivan
Comba, João Luiz Dihl
Hirata, Nina
Schiavon Porto, Marcelo
Vital, Creto
Pagot, Christian Azambuja
Petronetto, Fabiano
Clua, Esteban
Cardeal, Flávio
e-Mail Addressaugusto.cesar.silva@usp.br
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ, Brazil
Date17-20 Oct. 2017
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Book TitleProceedings
Tertiary TypeUndergraduate Work
History (UTC)2017-09-04 19:38:19 :: augusto.cesar.silva@usp.br -> administrator ::
2022-05-18 22:18:24 :: administrator -> :: 2017
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Keywordsmorphological operators
local features
image operator learning
AbstractMorphological operators in image processing have a wide range of applications, like in medical imaging and document image analysis. The design of such operators are made, mainly, by a trial and error approach. Another method to design these operators consists in using machine learning algorithms to define a local transformation that represents an operator. Previous works used mainly the intensity values of the pixels as feature vectors in the machine learning algorithms. We propose to extract different features, calculated from the image, to create different feature vectors to be used in the machine learning algorithms. We experiment this approach in four different public datasets, and results show that different features have a significant impact on the learned operators, but, just like the operators, the feature that provides better results also depends on the dataset used.
Arrangementurlib.net > SDLA > Fonds > SIBGRAPI 2017 > Image operator learning...
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/8JMKD3MGPAW/3PJ5ECP
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3PJ5ECP
Languageen
Target Fileimage-operator-learning-camera-ready.pdf
User Groupaugusto.cesar.silva@usp.br
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3PKCC58
Citing Item Listsid.inpe.br/sibgrapi/2017/09.12.13.04 8
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination doi 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 versiontype volume


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