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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3PJSQNL
Repositorysid.inpe.br/sibgrapi/2017/09.09.16.33
Last Update2017:09.09.16.33.41 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2017/09.09.16.33.41
Metadata Last Update2022:05.18.22.18.25 (UTC) administrator
Citation KeyCavalinOliv:2017:ReTeCl
TitleA Review of Texture Classification Methods and Databases
FormatOn-line
Year2017
Access Date2024, Apr. 27
Number of Files1
Size1573 KiB
2. Context
Author1 Cavalin, Paulo
2 Oliveira, Luiz S.
Affiliation1 IBM Research
2 Universidade Federal do Paraná - UFPR
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 Addresspcavalin@br.ibm.com
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 TypeTutorial
History (UTC)2017-09-09 16:33:41 :: pcavalin@br.ibm.com -> administrator ::
2022-05-18 22:18:25 :: administrator -> :: 2017
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
KeywordsTexture recognition
Image recognition
Deep Learn- ing
AbstractIn this survey, we present a review of methods and resources for texture recognition, presenting the most common techniques that have been used in the recent decades, along with current tendencies. That said, this paper covers since the most traditional approaches, for instance texture descriptors such as gray-level co-occurence matrices (GLCM) and Local Binary Patterns (LBP), to more recent approaches such as Convolutional Neural Networks (CNN) and multi-scale patch-based recognition based on encoding approaches such as Fisher Vectors. In addition, we point out relevant references for benchmark datasets, which can help the reader develop and evaluate new methods.
Arrangementurlib.net > SDLA > Fonds > SIBGRAPI 2017 > A Review of...
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/3PJSQNL
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3PJSQNL
Languageen
Target Filesibgrapi_paper2017.pdf
User Grouppcavalin@br.ibm.com
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 7
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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