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		<identifier>8JMKD3MGPAW/3PHJJAP</identifier>
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		<citationkey>AmaralLimaVieiViei:2017:ReGeEs</citationkey>
		<title>Reconhecimento de gestos estáticos da mão usando a Transformada de Distância e aplicações em Libras</title>
		<format>On-line</format>
		<year>2017</year>
		<date>Oct. 17-20, 2017</date>
		<numberoffiles>1</numberoffiles>
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		<author>Amaral, Lucas,</author>
		<author>Lima, Givanildo,</author>
		<author>Vieira, Tiago,</author>
		<author>Vieira, Thales,</author>
		<affiliation>Universidade Federal de Alagoas</affiliation>
		<affiliation>Universidade Federal de Alagoas</affiliation>
		<affiliation>Universidade Federal de Alagoas</affiliation>
		<affiliation>Universidade Federal de Alagoas</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>lucasthund3r@gmail.com</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 30 (SIBGRAPI)</conferencename>
		<conferencelocation>Niterói, RJ</conferencelocation>
		<booktitle>Proceedings</booktitle>
		<publisher>Sociedade Brasileira de Computação</publisher>
		<publisheraddress>Porto Alegre</publisheraddress>
		<tertiarytype>Undergraduate Work</tertiarytype>
		<transferableflag>1</transferableflag>
		<keywords>Transformada de Distância, Redes Neurais Convolucionais, Gestos de Libras.</keywords>
		<abstract>In this paper we propose a method to recognize static hand gestures from depth images. We first segment the hand from the background, and then compute the Distance Transform to train a Convolutional Neural Network (CNN) that is later used to classify hand poses. In order to evaluate our method in a practical context, we collected a dataset containing 1400 images representing 14 different hand configurations representing signs of the Brazilian Sign Language (Libras). Our method achieved an average recognition rate of 96.42.</abstract>
		<language>pt</language>
		<targetfile>Artigo_Distancia.pdf</targetfile>
		<usergroup>lucasthund3r@gmail.com</usergroup>
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