Identity statement area | |
Reference Type | Conference Paper (Conference Proceedings) |
Site | sibgrapi.sid.inpe.br |
Identifier | 8JMKD3MGPAW/3PJKJM2 |
Repository | sid.inpe.br/sibgrapi/2017/09.08.01.03 |
Last Update | 2017:09.11.20.25.17 administrator |
Metadata | sid.inpe.br/sibgrapi/2017/09.08.01.03.07 |
Metadata Last Update | 2021:02.23.03.53.30 administrator |
Citation Key | PereiraDiasMedeRebo:2017:ClFaGo |
Title | Classification of Failures in Goat Leather Samples Using Computer Vision and Machine Learning  |
Format | On-line |
Year | 2017 |
Access Date | 2021, Mar. 02 |
Number of Files | 1 |
Size | 3203 KiB |
Context area | |
Author | 1 Pereira, Renato F. 2 Dias, Madson Luis D. 3 Medeiros, Claudio Marques de Sá 4 Rebouças Filho, Pedro Pedrosa |
Affiliation | 1 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 |
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 | pedrosarf@ifce.edu.br |
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 | Industry Application Paper |
History | 2017-09-08 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 2021-02-23 03:53:30 :: administrator -> :: 2017 |
Content and structure area | |
Is the master or a copy? | is the master |
Content Stage | completed |
Transferable | 1 |
Keywords | Goat Leather, Classification of Failures, Computer Vision, Machine Learning. |
Abstract | Textile 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. |
Arrangement | |
source Directory Content | manuscript.pdf | 07/09/2017 22:03 | 3.1 MiB | |
agreement Directory Content | |
Conditions of access and use area | |
data URL | http://urlib.net/rep/8JMKD3MGPAW/3PJKJM2 |
zipped data URL | http://urlib.net/zip/8JMKD3MGPAW/3PJKJM2 |
Language | en |
Target File | manuscript_Sibgrapi.pdf |
User Group | pedrosarf@ifce.edu.br |
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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