Close

1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPBW34M/3JMJ79L
Repositorysid.inpe.br/sibgrapi/2015/06.18.19.06
Last Update2015:06.18.19.07.06 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2015/06.18.19.06.04
Metadata Last Update2022:05.18.22.20.55 (UTC) administrator
DOI10.1109/SIBGRAPI.2015.26
Citation KeyBastosAngeLoul:2015:ReStGe
TitleRecognition of Static Gestures Applied to Brazilian Sign Language (Libras)
FormatOn-line
Year2015
Access Date2022, May 21
Number of Files1
Size431 KiB
2. Context
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, BA, Brazil
Date26-29 Aug. 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
2022-05-18 22:20:55 :: administrator -> :: 2015
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
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%.
Arrangementurlib.net > SDLA > SIBGRAPI 2015 > Recognition of Static...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
agreement.html 18/06/2015 16:06 0.7 KiB 
4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPBW34M/3JMJ79L
zipped data URLhttp://urlib.net/zip/8JMKD3MGPBW34M/3JMJ79L
Languageen
Target FilePID3766353.pdf
User Groupigorcrexito@gmail.com
Visibilityshown
Update Permissionnot transferred
5. Allied materials
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
6. Notes
Empty Fieldsaccessionnumber archivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume


Close