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Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPBW34M/3JNJ57B
Repositorysid.inpe.br/sibgrapi/2015/06.24.22.18
Last Update2015:06.24.22.18.36 (UTC) viniciusrpb@icmc.usp.br
Metadatasid.inpe.br/sibgrapi/2015/06.24.22.18.36
Metadata Last Update2020:02.19.02.14.04 (UTC) administrator
Citation KeyBorgesHamSilVieOli:2015:HiAcLe
TitleA Highly Accurate Level Set Approach for Segmenting Green Microalgae Images
FormatOn-line
Year2015
Access Date2021, Nov. 28
Number of Files1
Size769 KiB
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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
DateAug. 26-29, 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 ::
2020-02-19 02:14:04 :: administrator -> :: 2015
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Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
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.
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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
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Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPBW34M/3K24PF8
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
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