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Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPBW34M/3JMJ79L
Repositorysid.inpe.br/sibgrapi/2015/06.18.19.06
Last Update2015:06.18.19.07.06 (UTC) igorcrexito@gmail.com
Metadatasid.inpe.br/sibgrapi/2015/06.18.19.06.04
Metadata Last Update2020:02.19.02.14.03 (UTC) administrator
Citation KeyBastosAngeLoul:2015:ReStGe
TitleRecognition of Static Gestures Applied to Brazilian Sign Language (Libras)
FormatOn-line
Year2015
Access Date2021, Dec. 03
Number of Files1
Size431 KiB
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Author1 Bastos, Igor Leonardo Oliveira
2 Angelo, Michele Fúlvia
3 Loula, Angelo Conrado
Affiliation1 Federal University of Bahia - UFBA
2 State University of Feira de Santana - UEFS
3 State University of Feira de Santana - UEFS
EditorPapa, João Paulo
Sander, Pedro Vieira
Marroquim, Ricardo Guerra
Farrell, Ryan
e-Mail Addressigorcrexito@gmail.com
Conference NameConference on Graphics, Patterns and Images, 28 (SIBGRAPI)
Conference LocationSalvador
DateAug. 26-29, 2015
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2015-06-18 19:07:06 :: igorcrexito@gmail.com -> administrator :: 2015
2020-02-19 02:14:03 :: administrator -> :: 2015
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Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
KeywordsHistogram of Oriented Gradients
Zernike Invariant Moments
Neural Networks
Gesture Recognition
Libras
AbstractThis 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%.
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data URLhttp://urlib.net/ibi/8JMKD3MGPBW34M/3JMJ79L
zipped data URLhttp://urlib.net/zip/8JMKD3MGPBW34M/3JMJ79L
Languageen
Target FilePID3766353.pdf
User Groupigorcrexito@gmail.com
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Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPBW34M/3K24PF8
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
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