Identity statement area | |
Reference Type | Conference Paper (Conference Proceedings) |
Site | sibgrapi.sid.inpe.br |
Identifier | 8JMKD3MGPAW/3PJSQNL |
Repository | sid.inpe.br/sibgrapi/2017/09.09.16.33 |
Last Update | 2017:09.09.16.33.41 administrator |
Metadata | sid.inpe.br/sibgrapi/2017/09.09.16.33.41 |
Metadata Last Update | 2021:02.23.03.53.33 administrator |
Citation Key | CavalinOliv:2017:ReTeCl |
Title | A Review of Texture Classification Methods and Databases  |
Format | On-line |
Year | 2017 |
Access Date | 2021, Mar. 02 |
Number of Files | 1 |
Size | 1573 KiB |
Context area | |
Author | 1 Cavalin, Paulo 2 Oliveira, Luiz S. |
Affiliation | 1 IBM Research 2 Universidade Federal do Paraná - UFPR |
Editor | Torchelsen, 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 Address | pcavalin@br.ibm.com |
Conference Name | Conference on Graphics, Patterns and Images, 30 (SIBGRAPI) |
Conference Location | Niterói, RJ |
Date | Oct. 17-20, 2017 |
Book Title | Proceedings |
Publisher | Sociedade Brasileira de Computação |
Publisher City | Porto Alegre |
Tertiary Type | Tutorial |
History | 2017-09-09 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 Stage | completed |
Transferable | 1 |
Keywords | Texture recognition, Image recognition, Deep Learn- ing. |
Abstract | In 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. |
Arrangement | |
source Directory Content | there are no files |
agreement Directory Content | |
Conditions of access and use area | |
data URL | http://urlib.net/rep/8JMKD3MGPAW/3PJSQNL |
zipped data URL | http://urlib.net/zip/8JMKD3MGPAW/3PJSQNL |
Language | en |
Target File | sibgrapi_paper2017.pdf |
User Group | pcavalin@br.ibm.com |
Visibility | shown |
Update Permission | not transferred |
Allied materials area | |
Mirror Repository | sid.inpe.br/banon/2001/03.30.15.38.24 |
Next Higher Units | 8JMKD3MGPAW/3PKCC58 |
Host Collection | sid.inpe.br/banon/2001/03.30.15.38 |
Notes area | |
Empty Fields | accessionnumber archivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination doi edition electronicmailaddress group holdercode isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume |
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