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Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPAW/3PJKJM2
Repositorysid.inpe.br/sibgrapi/2017/09.08.01.03
Last Update2017:09.11.20.25.17 pedrosarf@ifce.edu.br
Metadatasid.inpe.br/sibgrapi/2017/09.08.01.03.07
Metadata Last Update2020:02.20.22.06.47 administrator
Citation KeyPereiraDiasMedeRebo:2017:ClFaGo
TitleClassification of Failures in Goat Leather Samples Using Computer Vision and Machine Learning
FormatOn-line
Year2017
Access Date2021, Jan. 25
Number of Files1
Size3203 KiB
Context area
Author1 Pereira, Renato F.
2 Dias, Madson Luis D.
3 Medeiros, Claudio Marques de Sá
4 Rebouças Filho, Pedro Pedrosa
Affiliation1 Programa de Pós-Graduação em Ciência da Computação do Instituto Federal de Educação, Ciência e Tecnologia do Ceará
2 Programa de Pós-Graduação em Ciência da Computação do Instituto Federal de Educação, Ciência e Tecnologia do Ceará
3 Programa de Pós-Graduação em Ciência da Computação do Instituto Federal de Educação, Ciência e Tecnologia do Ceará
4 Programa de Pós-Graduação em Ciência da Computação do Instituto Federal de Educação, Ciência e Tecnologia do Ceará
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 Addresspedrosarf@ifce.edu.br
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 TypeIndustry Application Paper
History2017-09-08 01:03:07 :: pedrosarf@ifce.edu.br -> administrator ::
2017-09-09 18:59:08 :: administrator -> pedrosarf@ifce.edu.br :: 2017
2017-09-11 20:25:17 :: pedrosarf@ifce.edu.br -> administrator :: 2017
2020-02-20 22:06:47 :: administrator -> :: 2017
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
KeywordsGoat Leather, Classification of Failures, Computer Vision, Machine Learning.
AbstractTextile industry has used goat skins in manufactur- ing products that require high quality control. Thus, a specialist performed a skins qualities classification to put a price on the goat leather sample, but this evaluation depends on whom evaluate. To reduce these divergences and to increase the productivity on the textile industry area, this paper presents a new approach to detect leather failure using feature extractor and machine learning classifiers. Also, a new feature extractor, called of Pixel Intensity Analyzer (PIA), is proposed for this application. Experiments were performed with a real data set comparing PIA with two other features extractors using machine learning classifiers with each one. In accuracy, the best approach was LBP with LS-SVM (RBF), but in processing time as a very important factor, since it is a real-time application to the industry, the PIA combined with ELM presents the best cost-effective because it also has excellent accuracy rates.
source Directory Content
manuscript.pdf 07/09/2017 22:03 3.1 MiB
agreement Directory Content
agreement.html 07/09/2017 22:03 1.2 KiB 
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data URLhttp://urlib.net/rep/8JMKD3MGPAW/3PJKJM2
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3PJKJM2
Languageen
Target Filemanuscript_Sibgrapi.pdf
User Grouppedrosarf@ifce.edu.br
Visibilityshown
Update Permissionnot transferred
Allied materials area
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3PJT9LS
8JMKD3MGPAW/3PKCC58
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
Notes area
Empty Fieldsaccessionnumber 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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