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Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPAW/3PJSQNL
Repositorysid.inpe.br/sibgrapi/2017/09.09.16.33
Last Update2017:09.09.16.33.41 administrator
Metadatasid.inpe.br/sibgrapi/2017/09.09.16.33.41
Metadata Last Update2021:02.23.03.53.33 administrator
Citation KeyCavalinOliv:2017:ReTeCl
TitleA Review of Texture Classification Methods and Databases
FormatOn-line
Year2017
Access Date2021, Mar. 02
Number of Files1
Size1573 KiB
Context area
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
DateOct. 17-20, 2017
Book TitleProceedings
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Tertiary TypeTutorial
History2017-09-09 16:33:41 :: pcavalin@br.ibm.com -> administrator ::
2021-02-23 03:53:33 :: administrator -> :: 2017
Content and structure area
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.
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data URLhttp://urlib.net/rep/8JMKD3MGPAW/3PJSQNL
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3PJSQNL
Languageen
Target Filesibgrapi_paper2017.pdf
User Grouppcavalin@br.ibm.com
Visibilityshown
Update Permissionnot transferred
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Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3PKCC58
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
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