<?xml version="1.0" encoding="ISO-8859-1"?>
<metadatalist>
	<metadata ReferenceType="Conference Proceedings">
		<identifier>8JMKD3MGPBW34M/3EE7ACL</identifier>
		<repository>sid.inpe.br/sibgrapi/2013/07.08.23.00</repository>
		<metadatarepository>sid.inpe.br/sibgrapi/2013/07.08.23.00.49</metadatarepository>
		<site>sibgrapi.sid.inpe.br 802</site>
		<citationkey>FariasFariMarrClua:2013:PaImSe</citationkey>
		<author>Farias, Renato,</author>
		<author>Farias, Ricardo,</author>
		<author>Marroquim, Ricardo,</author>
		<author>Clua, Esteban,</author>
		<affiliation>Universidade Federal do Rio de Janeiro</affiliation>
		<affiliation>Universidade Federal do Rio de Janeiro</affiliation>
		<affiliation>Universidade Federal do Rio de Janeiro</affiliation>
		<affiliation>Universidade Federal Fluminense</affiliation>
		<title>Parallel image segmentation using reduction-sweeps on multicore processors and GPUs</title>
		<conferencename>Conference on Graphics, Patterns and Images, 26 (SIBGRAPI)</conferencename>
		<year>2013</year>
		<editor>Boyer, Kim,</editor>
		<editor>Hirata, Nina,</editor>
		<editor>Nedel, Luciana,</editor>
		<editor>Silva, Claudio,</editor>
		<booktitle>Proceedings</booktitle>
		<date>Aug. 5-8, 2013</date>
		<publisheraddress>Los Alamitos</publisheraddress>
		<publisher>IEEE Computer Society</publisher>
		<conferencelocation>Arequipa, Peru</conferencelocation>
		<keywords>Image segmentation, computer vision, GPU programming, parallel programming.</keywords>
		<abstract>In 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.</abstract>
		<language>en</language>
		<tertiarytype>Full Paper</tertiarytype>
		<format>On-line.</format>
		<size>940 KiB</size>
		<numberoffiles>1</numberoffiles>
		<targetfile>sibgrapi-camera-ready-no-bookmarks.pdf</targetfile>
		<lastupdate>2013:07.08.23.00.49 sid.inpe.br/banon/2001/03.30.15.38 renatomdf@gmail.com</lastupdate>
		<metadatalastupdate>2020:02.19.03.09.22 sid.inpe.br/banon/2001/03.30.15.38 administrator {D 2013}</metadatalastupdate>
		<mirrorrepository>sid.inpe.br/banon/2001/03.30.15.38.24</mirrorrepository>
		<e-mailaddress>renatomdf@gmail.com</e-mailaddress>
		<usergroup>renatomdf@gmail.com</usergroup>
		<visibility>shown</visibility>
		<transferableflag>1</transferableflag>
		<hostcollection>sid.inpe.br/banon/2001/03.30.15.38</hostcollection>
		<contenttype>External Contribution</contenttype>
		<agreement>agreement.html .htaccess .htaccess2</agreement>
		<lasthostcollection>sid.inpe.br/banon/2001/03.30.15.38</lasthostcollection>
		<url>http://sibgrapi.sid.inpe.br/rep-/sid.inpe.br/sibgrapi/2013/07.08.23.00</url>
	</metadata>
</metadatalist>