@InProceedings{PereiraDiasMedeRebo:2017:ClFaGo,
author = "Pereira, Renato F. and Dias, Madson Luis D. and Medeiros, Claudio
Marques de S{\'a} and Rebou{\c{c}}as Filho, Pedro Pedrosa",
affiliation = "Programa de P{\'o}s-Gradua{\c{c}}{\~a}o em Ci{\^e}ncia da
Computa{\c{c}}{\~a}o do Instituto Federal de
Educa{\c{c}}{\~a}o, Ci{\^e}ncia e Tecnologia do Cear{\'a} and
Programa de P{\'o}s-Gradua{\c{c}}{\~a}o em Ci{\^e}ncia da
Computa{\c{c}}{\~a}o do Instituto Federal de
Educa{\c{c}}{\~a}o, Ci{\^e}ncia e Tecnologia do Cear{\'a} and
Programa de P{\'o}s-Gradua{\c{c}}{\~a}o em Ci{\^e}ncia da
Computa{\c{c}}{\~a}o do Instituto Federal de
Educa{\c{c}}{\~a}o, Ci{\^e}ncia e Tecnologia do Cear{\'a} and
Programa de P{\'o}s-Gradua{\c{c}}{\~a}o em Ci{\^e}ncia da
Computa{\c{c}}{\~a}o do Instituto Federal de
Educa{\c{c}}{\~a}o, Ci{\^e}ncia e Tecnologia do Cear{\'a}",
title = "Classification of Failures in Goat Leather Samples Using Computer
Vision and Machine Learning",
booktitle = "Proceedings...",
year = "2017",
editor = "Torchelsen, Rafael Piccin and Nascimento, Erickson Rangel do and
Panozzo, Daniele and Liu, Zicheng and Farias, Myl{\`e}ne and
Viera, Thales and Sacht, Leonardo and Ferreira, Nivan and Comba,
Jo{\~a}o Luiz Dihl and Hirata, Nina and Schiavon Porto, Marcelo
and Vital, Creto and Pagot, Christian Azambuja and Petronetto,
Fabiano and Clua, Esteban and Cardeal, Fl{\'a}vio",
organization = "Conference on Graphics, Patterns and Images, 30. (SIBGRAPI)",
publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
address = "Porto Alegre",
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.",
conference-location = "Niter{\'o}i, RJ, Brazil",
conference-year = "17-20 Oct. 2017",
language = "en",
ibi = "8JMKD3MGPAW/3PJKJM2",
url = "http://urlib.net/ibi/8JMKD3MGPAW/3PJKJM2",
targetfile = "manuscript_Sibgrapi.pdf",
urlaccessdate = "2024, Apr. 27"
}