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		<citationkey>PereiraDiasMedeRebo:2017:ClFaGo</citationkey>
		<title>Classification of Failures in Goat Leather Samples Using Computer Vision and Machine Learning</title>
		<format>On-line</format>
		<year>2017</year>
		<numberoffiles>1</numberoffiles>
		<size>3203 KiB</size>
		<author>Pereira, Renato F.,</author>
		<author>Dias, Madson Luis D.,</author>
		<author>Medeiros, Claudio Marques de Sá,</author>
		<author>Rebouças Filho, Pedro Pedrosa,</author>
		<affiliation>Programa de Pós-Graduação em Ciência da Computação do Instituto Federal de Educação, Ciência e Tecnologia do Ceará</affiliation>
		<affiliation>Programa de Pós-Graduação em Ciência da Computação do Instituto Federal de Educação, Ciência e Tecnologia do Ceará</affiliation>
		<affiliation>Programa de Pós-Graduação em Ciência da Computação do Instituto Federal de Educação, Ciência e Tecnologia do Ceará</affiliation>
		<affiliation>Programa de Pós-Graduação em Ciência da Computação do Instituto Federal de Educação, Ciência e Tecnologia do Ceará</affiliation>
		<editor>Torchelsen, Rafael Piccin,</editor>
		<editor>Nascimento, Erickson Rangel do,</editor>
		<editor>Panozzo, Daniele,</editor>
		<editor>Liu, Zicheng,</editor>
		<editor>Farias, Mylène,</editor>
		<editor>Viera, Thales,</editor>
		<editor>Sacht, Leonardo,</editor>
		<editor>Ferreira, Nivan,</editor>
		<editor>Comba, João Luiz Dihl,</editor>
		<editor>Hirata, Nina,</editor>
		<editor>Schiavon Porto, Marcelo,</editor>
		<editor>Vital, Creto,</editor>
		<editor>Pagot, Christian Azambuja,</editor>
		<editor>Petronetto, Fabiano,</editor>
		<editor>Clua, Esteban,</editor>
		<editor>Cardeal, Flávio,</editor>
		<e-mailaddress>pedrosarf@ifce.edu.br</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 30 (SIBGRAPI)</conferencename>
		<conferencelocation>Niterói, RJ</conferencelocation>
		<date>Oct. 17-20, 2017</date>
		<booktitle>Proceedings</booktitle>
		<publisher>Sociedade Brasileira de Computação</publisher>
		<publisheraddress>Porto Alegre</publisheraddress>
		<tertiarytype>Industry Application Paper</tertiarytype>
		<transferableflag>1</transferableflag>
		<keywords>Goat Leather, Classification of Failures, Computer Vision, Machine Learning.</keywords>
		<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.</abstract>
		<language>en</language>
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		<usergroup>pedrosarf@ifce.edu.br</usergroup>
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