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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPBW34M/3869J8L
Repositorysid.inpe.br/sibgrapi/2010/08.28.19.42
Last Update2010:08.28.19.42.10 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2010/08.28.19.42.10
Metadata Last Update2022:06.14.00.06.51 (UTC) administrator
DOI10.1109/SIBGRAPI.2010.30
Citation KeySpinaFalc:2010:InUnUs
TitleIntelligent understanding of user input applied to arc-weight estimation for graph-based foreground segmentation
FormatPrinted, On-line.
Year2010
Access Date2024, May 04
Number of Files1
Size737 KiB
2. Context
Author1 Spina, Thiago Vallin
2 Falcão, Alexandre Xavier
Affiliation1 Institute of Computing - University of Campinas
2 Institute of Computing - University of Campinas
EditorBellon, Olga
Esperança, Claudio
e-Mail Addresstvspina@liv.ic.unicamp.br
Conference NameConference on Graphics, Patterns and Images, 23 (SIBGRAPI)
Conference LocationGramado, RS, Brazil
Date30 Aug.-3 Sep. 2010
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2010-10-01 04:19:37 :: tvspina@liv.ic.unicamp.br -> administrator :: 2010
2022-06-14 00:06:51 :: administrator -> :: 2010
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordsgraph-based image segmentation
intelligent arc-weight estimation
image foresting transform
fuzzy supervised classification
clustering
multiscale image filtering
AbstractWe present an intelligent approach for understanding user interaction to simplify the interface of graph-based image segmentation. The user draws a set of markers (strokes) over the object and the background, and our method automatically determines a subset of these pixels with dissimilar image properties for arc-weight estimation. Arc-weight estimation combines object information learned from the set of selected pixels with image information, to make object delineation more effective. Our method differs from approaches that recompute the arc weights carelessly during delineation, by further interpreting user interaction to determine when and where to recompute them. Furthermore, we build our framework around the image foresting transform (IFT), by taking advantage of its operators for supervised fuzzy classification, clustering, and object delineation. We evaluate our framework using a dataset with 50 natural images and by comparing it against another recent IFT-based method, which computes arc weights in a separated step of user interaction for more effective segmentation.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2010 > Intelligent understanding of...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPBW34M/3869J8L
zipped data URLhttp://urlib.net/zip/8JMKD3MGPBW34M/3869J8L
Languageen
Target FilePID1393191.pdf
User Grouptvspina@liv.ic.unicamp.br
Visibilityshown
5. Allied materials
Next Higher Units8JMKD3MGPEW34M/46SJT6B
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2022/05.14.20.21 6
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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