author = "Medeiros Neto, Francisco Gerardo and Braga, {\'{\I}}talo 
                         Rodrigues and Harber, Matthew Henry and J{\'u}nior, I{\'a}lis 
                         Cavalcante de Paula",
          affiliation = "{Federal University of Cear{\'a}} and {Federal University of 
                         Cear{\'a}} and GeoPoll and {Federal University of Cear{\'a}}",
                title = "Drosophila melanogaster Gender Classification Based on Fractal 
            booktitle = "Proceedings...",
                 year = "2017",
               editor = "Torchelsen, Rafael Piccin and Nascimento, Erickson Rangel do and 
                         Panozzo, Daniele and Liu, Zicheng and Farias, Myl{\`e}ne and 
                         Viera, Thales and Sacht, Leonardo and Ferreira, Nivan and Comba, 
                         Jo{\~a}o Luiz Dihl and Hirata, Nina and Schiavon Porto, Marcelo 
                         and Vital, Creto and Pagot, Christian Azambuja and Petronetto, 
                         Fabiano and Clua, Esteban and Cardeal, Fl{\'a}vio",
         organization = "Conference on Graphics, Patterns and Images, 30. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "stationary wavelet transform, Canny filter, fractal dimension, 
             abstract = "Biometrics, 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).",
  conference-location = "Niter{\'o}i, RJ",
      conference-year = "Oct. 17-20, 2017",
             language = "en",
           targetfile = "sibgrapi-2017-cr.pdf",
        urlaccessdate = "2021, Jan. 26"