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1. Identity statement
Reference TypeBook Section
Siteplutao.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W/3FCKULS
Repositorysid.inpe.br/plutao/2013/12.12.14.52.45   (restricted access)
Last Update2013:12.30.17.32.39 (UTC) simone
Metadata Repositorysid.inpe.br/plutao/2013/12.12.14.52.46
Metadata Last Update2024:01.10.11.21.50 (UTC) simone
DOI10.1007/978-3-319-02895-8_45
ISBN9783319028941
Labellattes: 5123287769635741 3 KörtingCastFons:2013:DiSeMe
Citation KeyKortingCastFons:2013:DiSeMe
TitleThe Divide and Segment Method for Parallel Image Segmentation
Year2013
Access Date2024, Apr. 28
Secondary TypePRE LI
Number of Files1
Size12886 KiB
2. Context
Author1 Korting, Thales Sehn
2 Castejon, Emiliano Ferreira
3 Fonseca, Leila Maria Garcia
Resume Identifier1
2
3 8JMKD3MGP5W/3C9JHLD
Group1 DPI-OBT-INPE-MCTI-GOV-BR
2
3 DPI-OBT-INPE-MCTI-GOV-BR
Affiliation1 Instituto Nacional de Pesquisas Espaciais (INPE)
2
3 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1 thales@dpi.inpe.br
2
3 leila@dpi.inpe.br
EditorBlanc-Talon, J.
Kasinski, A.
Philips, W.
Popescu, D.
Sheunders, P.
e-Mail Addressleila@dpi.inpe.br
Book TitleAdvanced concepts for intelligen vision systems
PublisherSpringer
CityBerlin
Pages504-515
History (UTC)2013-12-16 16:13:39 :: lattes -> administrator :: 2013
2014-01-09 13:38:04 :: administrator -> marcelo.pazos@sid.inpe.br :: 2013
2014-01-10 13:45:41 :: marcelo.pazos@sid.inpe.br -> administrator :: 2013
2024-01-10 11:12:09 :: administrator -> simone :: 2013
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
Keywordsparallel image segmentation
remote sensing
large spatial dimensions
AbstractRemote sensing images with large spatial dimensions are usual. Besides, they also include a diversity of spectral channels, increasing the volume of information. To obtain valuable information from remote sensing data, computers need higher amounts of memory and more efficient processing techniques. The first process in image analysis is segmentation, which identifies regions in images. Therefore, segmentation algorithms must deal with large amounts of data. Even with current computational power, certain image sizes may exceed the memory limits, which ask for different solutions. An alternative to overcome such limits is to employ the well-known divide and conquer strategy, by splitting the image into chunks, and segmenting each one individually. However, it arises the problem of merging neighboring chunks and keeping the homogeneity in such regions. In this work, we propose an alternative to divide the image into chunks by defining noncrisp borders between them. The noncrisp borders are computed based on Dijkstra algorithm, which is employed to find the shortest path between detected edges in the images. By applying our method, we avoid the postprocessing of neighboring regions, and therefore speed up the final segmentation.
AreaSRE
Arrangementurlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDPI > The Divide and...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Contentthere are no files
4. Conditions of access and use
Languagept
Target Filekorting2013divide.pdf
User Groupadministrator
lattes
marcelo.pazos@inpe.br
self-uploading-INPE-MCTI-GOV-BR
Reader Groupadministrator
simone
Visibilityshown
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
5. Allied materials
Mirror Repositoryiconet.com.br/banon/2006/11.26.21.31
Next Higher Units8JMKD3MGPCW/3EQCCU5
Citing Item Listsid.inpe.br/bibdigital/2013/09.09.15.05 1
URL (untrusted data)http://link.springer.com/10.1007/978-3-319-02895-8_45
Host Collectiondpi.inpe.br/plutao@80/2008/08.19.15.01
6. Notes
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