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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPBW34M/3EEQR3H
Repositorysid.inpe.br/sibgrapi/2013/07.12.22.55
Last Update2013:07.12.22.55.42 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2013/07.12.22.55.42
Metadata Last Update2022:06.14.00.07.53 (UTC) administrator
DOI10.1109/SIBGRAPI.2013.10
Citation KeyOtiniano-RodríguezCáma:2013:FiSpRe
TitleFinger Spelling Recognition from RGB-D Information using Kernel Descriptor
FormatOn-line.
Year2013
Access Date2024, Apr. 29
Number of Files1
Size1580 KiB
2. Context
Author1 Otiniano-Rodríguez, K.
2 Cámara-Chávez, G.
Affiliation1 Federal University of Ouro Preto
2 Federal University of Ouro Preto
EditorBoyer, Kim
Hirata, Nina
Nedel, Luciana
Silva, Claudio
e-Mail Addresskarlaotiniano@gmail.com
Conference NameConference on Graphics, Patterns and Images, 26 (SIBGRAPI)
Conference LocationArequipa, Peru
Date5-8 Aug. 2013
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2013-07-12 22:55:42 :: karlaotiniano@gmail.com -> administrator ::
2022-06-14 00:07:53 :: administrator -> :: 2013
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordssign language
finger spelling
support vector machine (SVM)
bag-of-visual-words
AbstractDeaf people use systems of communication based on sign language and finger spelling. Manual spelling, or finger spelling, is a system where each letter of the alphabet is represented by an unique and discrete movement of the hand. RGB and depth images can be used to characterize hand shapes corresponding to letters of the alphabet. The advantage of depth cameras over color cameras for gesture recognition is more evident when performing hand segmentation. In this paper, we propose a hybrid system approach for finger spelling recognition using RGB-D information from Kinect sensor. In a first stage, the hand area is segmented from background using depth map and precise hand shape is extracted using both depth data and color data from Kinect sensor. Motivated by the performance of kernel based features, due to its simplicity and the ability to turn any type of pixel attribute into patch-level features, we decided to use the gradient kernel descriptor for feature extraction from depth images. The Scale-Invariant Feature Transform (SIFT) is used for describing the content of the RGB image. Then, the Bag-of-Visual-Words approach is used to extract semantic information. Finally, these features are used as input of our Support Vector Machine (SVM) classifier. The performance of this approach is quantitatively and qualitatively evaluated on a dataset of real images of American Sign Language (ASL) hand shapes. Three experiments were performed, using a combination of RGB and depth information and also using only RGB or depth information separately. The database used is composed of 120,000 images. According to our experiments, our approach has an accuracy rate of 91.26% when RGB and depth information is used, outperforming other state-of-the-art methods.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2013 > Finger Spelling Recognition...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Finger Spelling Recognition...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPBW34M/3EEQR3H
zipped data URLhttp://urlib.net/zip/8JMKD3MGPBW34M/3EEQR3H
Languageen
Target Filefinal paper.pdf
User Groupkarlaotiniano@gmail.com
Visibilityshown
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/46SLB4P
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2022/05.15.04.02 9
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage 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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