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		<identifier>8JMKD3MGPBW34M/3JMJ79L</identifier>
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		<lastupdate>2015:06.18.19.07.06 sid.inpe.br/banon/2001/03.30.15.38 igorcrexito@gmail.com</lastupdate>
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		<citationkey>BastosAngeLoul:2015:ReStGe</citationkey>
		<title>Recognition of Static Gestures Applied to Brazilian Sign Language (Libras)</title>
		<format>On-line</format>
		<year>2015</year>
		<numberoffiles>1</numberoffiles>
		<size>431 KiB</size>
		<author>Bastos, Igor Leonardo Oliveira,</author>
		<author>Angelo, Michele Fúlvia,</author>
		<author>Loula, Angelo Conrado,</author>
		<affiliation>Federal University of Bahia - UFBA</affiliation>
		<affiliation>State University of Feira de Santana - UEFS</affiliation>
		<affiliation>State University of Feira de Santana - UEFS</affiliation>
		<editor>Papa, João Paulo,</editor>
		<editor>Sander, Pedro Vieira,</editor>
		<editor>Marroquim, Ricardo Guerra,</editor>
		<editor>Farrell, Ryan,</editor>
		<e-mailaddress>igorcrexito@gmail.com</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 28 (SIBGRAPI)</conferencename>
		<conferencelocation>Salvador</conferencelocation>
		<date>Aug. 26-29, 2015</date>
		<publisher>IEEE Computer Society</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Full Paper</tertiarytype>
		<transferableflag>1</transferableflag>
		<contenttype>External Contribution</contenttype>
		<keywords>Histogram of Oriented Gradients, Zernike Invariant Moments, Neural Networks, Gesture Recognition, Libras.</keywords>
		<abstract>This paper aims at describing an approach developed for the recognition of gestures on digital images. In this way, two shape descriptors were used: the histogram of oriented gradients (HOG) and Zernike invariant moments (ZIM). A feature vector composed by the information acquired with both descriptors was used to train and test a two stage Neural Network, which is responsible for performing the recognition. In order to evaluate the approach in a practical context, a dataset containing 9600 images representing 40 different gestures (signs) from Brazilian Sign Language (Libras) was composed. This approach showed high recognition rates (hit rates), reaching a final average of 96.77%.</abstract>
		<language>en</language>
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