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Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPAW/3PHJJAP
Repositorysid.inpe.br/sibgrapi/2017/09.01.15.53
Last Update2017:09.01.15.53.38 lucasthund3r@gmail.com
Metadatasid.inpe.br/sibgrapi/2017/09.01.15.53.38
Metadata Last Update2020:02.20.22.06.46 administrator
Citation KeyAmaralLimaVieiViei:2017:ReGeEs
TitleReconhecimento de gestos estáticos da mão usando a Transformada de Distância e aplicações em Libras
FormatOn-line
Year2017
DateOct. 17-20, 2017
Access Date2021, Jan. 21
Number of Files1
Size958 KiB
Context area
Author1 Amaral, Lucas
2 Lima, Givanildo
3 Vieira, Tiago
4 Vieira, Thales
Affiliation1 Universidade Federal de Alagoas
2 Universidade Federal de Alagoas
3 Universidade Federal de Alagoas
4 Universidade Federal de Alagoas
EditorTorchelsen, 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 Addresslucasthund3r@gmail.com
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ
Book TitleProceedings
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Tertiary TypeUndergraduate Work
History2017-09-01 15:53:38 :: lucasthund3r@gmail.com -> administrator ::
2020-02-20 22:06:46 :: administrator -> :: 2017
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
KeywordsTransformada de Distância, Redes Neurais Convolucionais, Gestos de Libras.
AbstractIn 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.
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data URLhttp://urlib.net/rep/8JMKD3MGPAW/3PHJJAP
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3PHJJAP
Languagept
Target FileArtigo_Distancia.pdf
User Grouplucasthund3r@gmail.com
Visibilityshown
Update Permissionnot transferred
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Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3PJT9LS
8JMKD3MGPAW/3PKCC58
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
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