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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPBW4/35SLJKB
Repositorysid.inpe.br/sibgrapi@80/2009/08.21.02.57
Last Update2009:08.21.02.57.45 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi@80/2009/08.21.02.57.46
Metadata Last Update2022:06.14.00.14.05 (UTC) administrator
DOI10.1109/SIBGRAPI.2009.45
Citation KeySpinaMAFZPCF:2009:CoStAm
TitleA comparative study among pattern classifiers in interactive image segmentation
FormatPrinted, On-line.
Year2009
Access Date2024, Apr. 30
Number of Files1
Size640 KiB
2. Context
Author1 Spina, Thiago Vallin
2 Montoya-Zegarra, Javier Alexander
3 Andrijauskas, Fabio
4 Faria, Fábio Augusto
5 Zampieri, Carlos Elias Arminio
6 Pinto-Cáceres, Sheila Maricela
7 Carvalho, Tiago José
8 Falcão, Alexandre Xavier
Affiliation1 University of Campinas
2 University of Campinas
3 University of Campinas
4 University of Campinas
5 University of Campinas
6 University of Campinas
7 University of Campinas
8 University of Campinas
EditorNonato, Luis Gustavo
Scharcanski, Jacob
e-Mail Addressthiago.spina@gmail.com
Conference NameBrazilian Symposium on Computer Graphics and Image Processing, 22 (SIBGRAPI)
Conference LocationRio de Janeiro, RJ, Brazil
Date11-14 Oct. 2009
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2010-08-28 20:03:27 :: thiago.spina@gmail.com -> administrator ::
2022-06-14 00:14:05 :: administrator -> :: 2009
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordspattern classifiers
graph-based image segmentation
image foresting transform
fuzzy classification
AbstractEdition of natural images usually asks for considerable user involvement, being segmentation one of the main challenges. This paper describes an unified graph-based framework for fast, precise and accurate interactive image segmentation. The method divides segmentation into object recognition, enhancement and extraction. Recognition is done by the user when markers are selected inside and outside the object. Enhancement increases the dissimilarities between object and background and Extraction separates them. Enhancement is done by a fuzzy pixel classifier and it has a great impact in the number of markers required for extraction. In view of minimizing user involvement, we focus this paper on a comparative study among popular classifiers for enhancement, conducting experiments with several natural images and seven users. .
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2009 > A comparative study...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > A comparative study...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Contentthere are no files
4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPBW4/35SLJKB
zipped data URLhttp://urlib.net/zip/8JMKD3MGPBW4/35SLJKB
Languageen
Target FilePID964061.pdf
User Groupthiago.spina@gmail.com
Visibilityshown
5. Allied materials
Next Higher Units8JMKD3MGPEW34M/46SJQ2S
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2022/06.10.21.49 2
sid.inpe.br/sibgrapi/2022/05.14.19.43 2
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 mirrorrepository 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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