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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3PHJJAP
Repositorysid.inpe.br/sibgrapi/2017/09.01.15.53
Last Update2017:09.01.15.53.38 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2017/09.01.15.53.38
Metadata Last Update2022:05.18.22.18.23 (UTC) administrator
Citation KeyAmaralLimaVieiViei:2017:ReGeEs
TitleReconhecimento de gestos estáticos da mão usando a Transformada de Distância e aplicações em Libras
FormatOn-line
Year2017
Access Date2024, Oct. 08
Number of Files1
Size958 KiB
2. Context
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, Brazil
Date17-20 Oct. 2017
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Book TitleProceedings
Tertiary TypeUndergraduate Work
History (UTC)2017-09-01 15:53:38 :: lucasthund3r@gmail.com -> administrator ::
2022-05-18 22:18:23 :: administrator -> :: 2017
3. Content and structure
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.
Arrangementurlib.net > SDLA > Fonds > SIBGRAPI 2017 > Reconhecimento de gestos...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
agreement.html 01/09/2017 12:53 1.2 KiB 
4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3PHJJAP
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3PHJJAP
Languagept
Target FileArtigo_Distancia.pdf
User Grouplucasthund3r@gmail.com
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3PKCC58
Citing Item Listsid.inpe.br/sibgrapi/2017/09.12.13.04 54
sid.inpe.br/banon/2001/03.30.15.38.24 5
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination doi edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume


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