Identity statement area
Reference TypeConference Paper (Conference Proceedings)
Last Update2017: administrator
Metadata Last Update2020: administrator
Citation KeyMedeirosNetoBragHarbJúni:2017:DrMeGe
TitleDrosophila melanogaster Gender Classification Based on Fractal Dimension
DateOct. 17-20, 2017
Access Date2021, Jan. 19
Number of Files1
Size1350 KiB
Context area
Author1 Medeiros Neto, Francisco Gerardo
2 Braga, Ítalo Rodrigues
3 Harber, Matthew Henry
4 Júnior, Iális Cavalcante de Paula
Affiliation1 Federal University of Ceará
2 Federal University of Ceará
3 GeoPoll
4 Federal University of Ceará
EditorTorchelsen, Rafael Piccin
Nascimento, Erickson Rangel do
Panozzo, Daniele
Liu, Zicheng
Farias, Mylčne
Viera, Thales
Sacht, Leonardo
Ferreira, Nivan
Comba, Joăo Luiz Dihl
Hirata, Nina
Schiavon Porto, Marcelo
Vital, Creto
Pagot, Christian Azambuja
Petronetto, Fabiano
Clua, Esteban
Cardeal, Flávio
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2017-08-16 12:58:57 :: -> administrator ::
2020-02-19 02:01:18 :: administrator -> :: 2017
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
Keywordsstationary wavelet transform, Canny filter, fractal dimension, classification.
AbstractBiometrics, previously used only in human identification, can help experts in the analysis of biological images. Flies of the genus Drosophila have become model organisms by almost global presence and short life cycle. Facial recognition techniques and geometric morphometry can be used in image processing for classification. The latter requires human interaction. This work details a methodology based on stationary wavelet transform, Canny filter and fractal dimension aimed to infer the gender of Drosophila melanogaster based on images of their wings. The combination of variation in the training and test samples and classification methods showed the proposed algorithms accuracy rate, 90%, outperformed other methods. The proposed methodology proved efficient by using a reduced number of attributes and did not require human interaction for feature extraction (landmarks).
source Directory Contentthere are no files
agreement Directory Content
agreement.html 16/08/2017 09:58 1.2 KiB 
Conditions of access and use area
data URL
zipped data URL
Target Filesibgrapi-2017-cr.pdf
Update Permissionnot transferred
Allied materials area
Next Higher Units8JMKD3MGPAW/3PJT9LS
Notes area
Empty Fieldsaccessionnumber archivingpolicy archivist area callnumber copyholder copyright creatorhistory descriptionlevel dissemination doi edition electronicmailaddress group holdercode isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume