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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPBW34M/3JMP57H
Repositorysid.inpe.br/sibgrapi/2015/06.19.22.01
Last Update2015:06.19.22.01.36 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2015/06.19.22.01.36
Metadata Last Update2022:06.14.00.08.13 (UTC) administrator
DOI10.1109/SIBGRAPI.2015.17
Citation KeyJacquesJrMuss:2015:ImHeHu
TitleImproved head-shoulder human contour estimation through clusters of learned shape models
FormatOn-line
Year2015
Access Date2024, Apr. 20
Number of Files1
Size4654 KiB
2. Context
Author1 Jacques Junior, Julio Cezar Silveira
2 Musse, Soraia Raupp
Affiliation1 Pontifícia Universidade Católica do Rio Grande do Sul
2 Pontifícia Universidade Católica do Rio Grande do Sul
EditorPapa, João Paulo
Sander, Pedro Vieira
Marroquim, Ricardo Guerra
Farrell, Ryan
e-Mail Addressjuliojj@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)2015-06-19 22:01:36 :: juliojj@gmail.com -> administrator ::
2022-06-14 00:08:13 :: administrator -> :: 2015
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordshuman head-shoulder estimation
omega-shaped region
human segmentation
AbstractIn this paper we propose a clustering-based learning approach to improve an existing model for human head-shoulder contour estimation. The contour estimation is guided by a learned head-shoulder shape model, initialized automatically by a face detector. A dataset with labeled data is used to create the headshoulder shape model and to quantitatively analyze the results. In the proposed approach, geometric features are firstly extracted from the learning dataset. Then, the number of shape models to be learned is obtained by an unsupervised clustering algorithm. In the segmentation stage, different graphs with an omega-like shape are built around the detected face, related to each learned shape model. A path with maximal cost, related to each graph, defines a initial estimative of the head-shoulder contour. The final estimation is given by the path with maximum average energy. Experimental results indicate that the proposed technique outperformed the original model, which is based on a single shape model, learned in a more simple way. In addition, it achieved comparable accuracy to other state-of-the-art models.
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPBW34M/3JMP57H
zipped data URLhttp://urlib.net/zip/8JMKD3MGPBW34M/3JMP57H
Languageen
Target Filesib2015-camera-ready-pdf-express.pdf
User Groupjuliojj@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
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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