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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPBW34M/3JNJ57B
Repositorysid.inpe.br/sibgrapi/2015/06.24.22.18
Last Update2015:06.24.22.18.36 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2015/06.24.22.18.36
Metadata Last Update2022:06.14.00.08.16 (UTC) administrator
DOI10.1109/SIBGRAPI.2015.33
Citation KeyBorgesHamSilVieOli:2015:HiAcLe
TitleA Highly Accurate Level Set Approach for Segmenting Green Microalgae Images
FormatOn-line
Year2015
Access Date2024, Apr. 25
Number of Files1
Size769 KiB
2. Context
Author1 Borges, Vinicius Ruela Pereira
2 Hamman, Bernd
3 Silva, Thais Garcia
4 Vieira, Armando Augusto Henriques
5 Oliveira, Maria Cristina Ferreira de
Affiliation1 University of Sao Paulo
2 University of California, Davis
3 Federal University of Sao Carlos
4 Federal University of Sao Carlos
5 University of Sao Paulo
EditorPapa, Joćo Paulo
Sander, Pedro Vieira
Marroquim, Ricardo Guerra
Farrell, Ryan
e-Mail Addressviniciusrpb@icmc.usp.br
Conference NameConference on Graphics, Patterns and Images, 28 (SIBGRAPI)
Conference LocationSalvador, BA, Brazil
Date26-29 Aug. 2015
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2015-06-24 22:18:36 :: viniciusrpb@icmc.usp.br -> administrator ::
2022-06-14 00:08:16 :: administrator -> :: 2015
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordsedge detection
Gaussian distribution
green microalgae
image segmentation
level set
AbstractWe present a method for segmenting 2D microscopy images of freshwater green microalgae. Our approach is based on a specialized level set method, leading to efficient and highly accurate algae segmentation. The level set formulation of our problem allows us to generate an algae's boundary curve as the result of an evolving level curve, based on computed background and algae regions in a given image. By characterizing the distributions of image intensity values in local regions, we are able to automatically classify image regions into background and algae regions. We present results obtained with our method. These results are very promising as they document that we can achieve highly accurate algae segmentations when comparing ours against manually segmented images (segmented by an expert biologist) and with results derived by other approaches covered in the literature.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2015 > A Highly Accurate...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > A Highly Accurate...
doc Directory Contentaccess
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPBW34M/3JNJ57B
zipped data URLhttp://urlib.net/zip/8JMKD3MGPBW34M/3JNJ57B
Languageen
Target FilePID3769253.pdf
User Groupviniciusrpb@icmc.usp.br
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPBW34M/3K24PF8
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2015/08.03.22.49 6
sid.inpe.br/banon/2001/03.30.15.38.24 1
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination 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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