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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPBW34M/3JNJB5B
Repositorysid.inpe.br/sibgrapi/2015/06.24.23.30
Last Update2015:06.24.23.30.24 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2015/06.24.23.30.24
Metadata Last Update2022:07.30.18.33.26 (UTC) administrator
DOI10.1109/SIBGRAPI.2015.50
Citation KeyRodríguezCahuAraúChav:2015:FiSpRe
TitleFinger Spelling Recognition using Kernel Descriptors and Depth Images
FormatOn-line
Year2015
Access Date2024, Apr. 30
Number of Files1
Size1597 KiB
2. Context
Author1 Rodríguez, Karla Catherine Otiniano
2 Cahuina, Edward Cayllahua
3 Araújo, Arnaldo de Albuquerque
4 Chavez, Guillermo Cámara
Affiliation1 Federal University of Minas Gerais
2 Federal University of Minas Gerais
3 Federal University of Minas Gerais
4 Federal University of Ouro Preto
EditorPapa, João Paulo
Sander, Pedro Vieira
Marroquim, Ricardo Guerra
Farrell, Ryan
e-Mail Addressecayllahua1@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)2015-06-24 23:30:24 :: ecayllahua1@gmail.com -> administrator ::
2022-07-30 18:33:26 :: administrator -> :: 2015
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordsfinger spelling
depth images
kernel descriptors
Bag-of-Words
AbstractDeaf people use systems of communication based on sign language and finger spelling. Finger spelling is a system where each letter of the alphabet is represented by a unique and discrete movement of the hand. RGB and depth images can be used to characterize hand shapes corresponding to letters of the alphabet. There exists an advantage of depth sensors, as Kinect, over color cameras for finger spelling recognition: depth images provide 3D information of the hand. In this paper, we propose a model for finger spelling recognition based on depth information using kernel descriptors, consisting of four stages. The performance of this approach is evaluated on a dataset of real images of the American Sign Language finger spelling. Different experiments were performed using a combination of both descriptors over depth information. Our approach obtains 92.92% of mean accuracy with 50% of samples for training; outperforming other state-of-the-art methods.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2015 > Finger Spelling Recognition...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Finger Spelling Recognition...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPBW34M/3JNJB5B
zipped data URLhttp://urlib.net/zip/8JMKD3MGPBW34M/3JNJB5B
Languageen
Target File113.pdf
User Groupecayllahua1@gmail.com
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPBW34M/3K24PF8
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2015/08.03.22.49 6
sid.inpe.br/banon/2001/03.30.15.38.24 2
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy 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 schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume


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