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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPEW34M/3U6AJF8
Repositorysid.inpe.br/sibgrapi/2019/10.02.13.22
Last Update2019:10.02.13.22.14 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2019/10.02.13.22.14
Metadata Last Update2022:06.14.00.09.44 (UTC) administrator
DOI10.1109/SIBGRAPI.2019.00015
Citation KeyGarciaMartLinsCama:2019:AcDiIm
TitleAcquisition of digital images and identification of Aedes aegypti mosquito eggs using classification and deep learning
FormatOn-line
Year2019
Access Date2024, Apr. 28
Number of Files1
Size8397 KiB
2. Context
Author1 Garcia, Pedro Saint Clair
2 Martins, Rafael
3 Lins Machado, George Luiz
4 Camara-Chavez, Guillermo
Affiliation1 Computer Science Department, Federal University of Ouro Preto
2 Biology Department, Federal University of Ouro Preto
3 Biology Department, Federal University of Ouro Preto
4 Computer Science Department, Federal University of Ouro Preto
EditorOliveira, Luciano Rebouças de
Sarder, Pinaki
Lage, Marcos
Sadlo, Filip
e-Mail Addressgcamarac@gmail.com
Conference NameConference on Graphics, Patterns and Images, 32 (SIBGRAPI)
Conference LocationRio de Janeiro, RJ, Brazil
Date28-31 Oct. 2019
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2019-10-02 13:22:14 :: gcamarac@gmail.com -> administrator ::
2022-06-14 00:09:44 :: administrator -> gcamarac@gmail.com :: 2019
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsAedes aegypti egg counting
mosquito eggs
deep learning
AbstractThe mosquito Aedes aegypti can transmit some diseases, which makes the study of the proliferation of this vector a necessary task. With the use of traps made in the laboratory, called ovitraps, it is possible to map egg deposition in a community. Through a camera, coupled with a magnifying glass, are acquired images containing the elements (eggs) to be counted. First, the goal is to find pixels with a similar color to mosquito eggs; for that, we take advantage of the slice color method. From these already worked images, a process of transfer learning with a convolutional neural network (CNN) is carried out. The intention is to separate which elements are eggs from the others. In 10% of the test images, the count performed by the model, and the ground truth of the number of eggs was considered weakly correlated. This problem occurs in images that have a high density of eggs or appear black elements that resemble mosquito eggs, but they are not. For the remaining 90% of the test images, the counting was considered to be perfectly correlated.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2019 > Acquisition of digital...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Acquisition of digital...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/3U6AJF8
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/3U6AJF8
Languageen
Target FilePaper ID 103.pdf
User Groupgcamarac@gmail.com
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/3UA4FNL
8JMKD3MGPEW34M/3UA4FPS
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2019/10.25.18.30.33 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
7. Description control
e-Mail (login)gcamarac@gmail.com
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