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Reference TypeConference Proceedings
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPBW34M/3EE7ACL
Repositorysid.inpe.br/sibgrapi/2013/07.08.23.00
Last Update2013:07.08.23.00.49 renatomdf@gmail.com
Metadatasid.inpe.br/sibgrapi/2013/07.08.23.00.49
Metadata Last Update2020:02.19.03.09.22 administrator
Citation KeyFariasFariMarrClua:2013:PaImSe
TitleParallel image segmentation using reduction-sweeps on multicore processors and GPUs
FormatOn-line.
Year2013
DateAug. 5-8, 2013
Access Date2020, Dec. 05
Number of Files1
Size940 KiB
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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
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
History2013-07-08 23:00:49 :: renatomdf@gmail.com -> administrator ::
2020-02-19 03:09:22 :: administrator -> :: 2013
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Is the master or a copy?is the master
Document Stagecompleted
Transferable1
Content TypeExternal Contribution
Tertiary TypeFull Paper
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.
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