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Metadata

1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPBW34M/3A3C4US
Repositorysid.inpe.br/sibgrapi/2011/07.09.02.10
Last Update2011:07.09.02.10.45 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2011/07.09.02.10.45
Metadata Last Update2022:06.14.00.07.11 (UTC) administrator
DOI10.1109/SIBGRAPI.2011.34
Citation KeyCasacaPaivNona:2011:SpSeUs
TitleSpectral Segmentation using Cartoon-Texture Decomposition and Inner Product-based metric
FormatDVD, On-line.
Year2011
Access Date2024, Apr. 30
Number of Files1
Size4403 KiB
2. Context
Author1 Casaca, Wallace
2 Paiva, Afonso
3 Nonato, Luis Gustavo
Affiliation1 Instituto de Ciências Matemáticas e de Computação – USP
2 Instituto de Ciências Matemáticas e de Computação – USP
3 Instituto de Ciências Matemáticas e de Computação – USP
EditorLewiner, Thomas
Torres, Ricardo
e-Mail Addresswallace.coc@gmail.com
Conference NameConference on Graphics, Patterns and Images, 24 (SIBGRAPI)
Conference LocationMaceió, AL, Brazil
Date28-31 Aug. 2011
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2011-07-23 15:36:12 :: wallace.coc@gmail.com -> administrator :: 2011
2022-06-14 00:07:11 :: administrator -> :: 2011
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsSpectral cut
image segmentation
similarity graph
cartoon-texture decomposition
harmonic analysis
normalized cuts
AbstractThis paper presents a user-assisted image partition technique that combines cartoon-texture decomposition, inner product-based similarity metric, and spectral cut into a unified framework. The cartoon-texture decomposition is used to first split the image into textured and texture-free components, the latter being used to define a gradient-based inner-product function. An affinity graph is then derived and weights are assigned to its edges according to the inner product-based metric. Spectral cut is computed on the affinity graph so as to partition the image. The computational burden of the spectral cut is mitigated by a fine-to-coarse image representation process, which enables moderate size graphs that can be handled more efficiently. The partitioning can be steered by interactively by changing the weights of the graph through user strokes. Weights are updated by combining the texture component computed in the first stage of our pipeline and a recent harmonic analysis technique that captures waving patterns. Finally, a coarse-to-fine interpolation is applied in order to project the partition back onto the original image. The suitable performance of the proposed methodology is attested by comparisons against state-of-art spectral segmentation methods.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2011 > Spectral Segmentation using...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Spectral Segmentation using...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPBW34M/3A3C4US
zipped data URLhttp://urlib.net/zip/8JMKD3MGPBW34M/3A3C4US
Languageen
Target FileSibgrapi_paper.pdf
User Groupwallace.coc@gmail.com
Visibilityshown
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/46SKNPE
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2022/05.15.00.56 5
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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