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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3MK9KBS
Repositorysid.inpe.br/sibgrapi/2016/10.14.18.31
Last Update2016:10.14.18.31.52 (UTC) igordsm@ime.usp.br
Metadata Repositorysid.inpe.br/sibgrapi/2016/10.14.18.31.52
Metadata Last Update2022:05.18.22.21.11 (UTC) administrator
Citation KeyMontagnerHiraJr:2016:ImOpLe
TitleImage operator learning and applications
FormatOn-line
Year2016
Access Date2024, May 03
Number of Files1
Size985 KiB
2. Context
Author1 Montagner, Igor S.
2 Hirata, Nina S. T.
3 Jr, Roberto Hirata
Affiliation1 University of São Paulo
2 University of São Paulo
3 University of São Paulo
EditorAliaga, Daniel G.
Davis, Larry S.
Farias, Ricardo C.
Fernandes, Leandro A. F.
Gibson, Stuart J.
Giraldi, Gilson A.
Gois, João Paulo
Maciel, Anderson
Menotti, David
Miranda, Paulo A. V.
Musse, Soraia
Namikawa, Laercio
Pamplona, Mauricio
Papa, João Paulo
Santos, Jefersson dos
Schwartz, William Robson
Thomaz, Carlos E.
e-Mail Addressigordsm@ime.usp.br
Conference NameConference on Graphics, Patterns and Images, 29 (SIBGRAPI)
Conference LocationSão José dos Campos, SP, Brazil
Date4-7 Oct. 2016
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Book TitleProceedings
Tertiary TypeTutorial
History (UTC)2016-10-14 18:31:52 :: igordsm@ime.usp.br -> administrator ::
2022-05-18 22:21:11 :: administrator -> :: 2016
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
KeywordsImage Operator learning
W-operators
Image Processing
Machine Learning
AbstractHigh-level understanding of image contents has been receiving much attention in the last decade. Low level processing figures as a building block in this framework and it also continues to play an important role in several specific tasks such as in image filtering and colorization, medical imaging, and document image processing. The design of image operators for these tasks is usually done manually by exploiting characteristics specific to the domain of application. An alternative design approach is to use machine learning techniques to estimate the transformations. Given pairs of images consisting of a typical input and respective desired output, the goal is to estimate an operator that transforms the inputs into the desired outputs. In this tutorial we present a rigorous mathematical formulation to the framework of learning locally defined and translation invariant transformations, practical procedures and strategies to address typical machine learning related issues, application examples, and current challenges. We also include information about the code used to generate the application examples.
Arrangementurlib.net > SDLA > Fonds > SIBGRAPI 2016 > 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/3MK9KBS
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3MK9KBS
Languageen
Target Filetutorial-final.pdf
User Groupigordsm@ime.usp.br
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3M2D4LP
Citing Item Listsid.inpe.br/sibgrapi/2016/07.02.23.50 5
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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