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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPBW34M/3EE7ACL
Repositorysid.inpe.br/sibgrapi/2013/07.08.23.00
Last Update2013:07.08.23.00.49 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2013/07.08.23.00.49
Metadata Last Update2022:06.14.00.07.46 (UTC) administrator
DOI10.1109/SIBGRAPI.2013.28
Citation KeyFariasFariMarrClua:2013:PaImSe
TitleParallel image segmentation using reduction-sweeps on multicore processors and GPUs
FormatOn-line.
Year2013
Access Date2024, Apr. 27
Number of Files1
Size940 KiB
2. Context
Author1 Farias, Renato
2 Farias, Ricardo
3 Marroquim, Ricardo
4 Clua, Esteban
Affiliation1 Universidade Federal do Rio de Janeiro
2 Universidade Federal do Rio de Janeiro
3 Universidade Federal do Rio de Janeiro
4 Universidade Federal Fluminense
EditorBoyer, Kim
Hirata, Nina
Nedel, Luciana
Silva, Claudio
e-Mail Addressrenatomdf@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)2013-07-08 23:00:49 :: renatomdf@gmail.com -> administrator ::
2022-06-14 00:07:46 :: administrator -> :: 2013
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsImage segmentation
computer vision
GPU programming
parallel programming
AbstractIn this paper we introduce the Reduction Sweep algorithm, a novel graph-based image segmentation algorithm that is designed for easy parallelization. It is based on a clustering approach focusing on local image characteristics. Each pixel is compared with its neighbors in an implicitly independent manner, and those deemed sufficiently similar according to a color criterion are joined. We achieve fast execution times while still maintaining the visual quality of the results. The algorithm is presented in four different implementations: sequential CPU, parallel CPU, GPU, and hybrid CPU-GPU. We compare the execution times of the four versions with each other and with other closely related image segmentation algorithms.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2013 > Parallel image segmentation...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Parallel image segmentation...
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/3EE7ACL
zipped data URLhttp://urlib.net/zip/8JMKD3MGPBW34M/3EE7ACL
Languageen
Target Filesibgrapi-camera-ready-no-bookmarks.pdf
User Grouprenatomdf@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 8
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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