@InProceedings{GarciaMartLinsCama:2019:AcDiIm,
               author = "Garcia, Pedro Saint Clair and Martins, Rafael and Lins Machado, 
                         George Luiz and Camara-Chavez, Guillermo",
          affiliation = "Computer Science Department, Federal University of Ouro Preto and 
                         Biology Department, Federal University of Ouro Preto and Biology 
                         Department, Federal University of Ouro Preto and Computer Science 
                         Department, Federal University of Ouro Preto",
                title = "Acquisition of digital images and identification of Aedes aegypti 
                         mosquito eggs using classification and deep learning",
            booktitle = "Proceedings...",
                 year = "2019",
               editor = "Oliveira, Luciano Rebou{\c{c}}as de and Sarder, Pinaki and Lage, 
                         Marcos and Sadlo, Filip",
         organization = "Conference on Graphics, Patterns and Images, 32. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Aedes aegypti egg counting, mosquito eggs, deep learning.",
             abstract = "The 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.",
  conference-location = "Rio de Janeiro, RJ, Brazil",
      conference-year = "28-31 Oct. 2019",
                  doi = "10.1109/SIBGRAPI.2019.00015",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2019.00015",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/3U6AJF8",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/3U6AJF8",
           targetfile = "Paper ID 103.pdf",
        urlaccessdate = "2024, Apr. 27"
}