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		<doi>10.1109/SIBGRAPI.2019.00015</doi>
		<citationkey>GarciaMartLinsCama:2019:AcDiIm</citationkey>
		<title>Acquisition of digital images and identification of Aedes aegypti mosquito eggs using classification and deep learning</title>
		<format>On-line</format>
		<year>2019</year>
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		<author>Garcia, Pedro Saint Clair,</author>
		<author>Martins, Rafael,</author>
		<author>Lins Machado, George Luiz,</author>
		<author>Camara-Chavez, Guillermo,</author>
		<affiliation>Computer Science Department, Federal University of Ouro Preto</affiliation>
		<affiliation>Biology Department, Federal University of Ouro Preto</affiliation>
		<affiliation>Biology Department, Federal University of Ouro Preto</affiliation>
		<affiliation>Computer Science Department, Federal University of Ouro Preto</affiliation>
		<editor>Oliveira, Luciano Rebouças de,</editor>
		<editor>Sarder, Pinaki,</editor>
		<editor>Lage, Marcos,</editor>
		<editor>Sadlo, Filip,</editor>
		<e-mailaddress>gcamarac@gmail.com</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 32 (SIBGRAPI)</conferencename>
		<conferencelocation>Rio de Janeiro, RJ, Brazil</conferencelocation>
		<date>28-31 Oct. 2019</date>
		<publisher>IEEE Computer Society</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Full Paper</tertiarytype>
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		<keywords>Aedes aegypti egg counting, mosquito eggs, deep learning.</keywords>
		<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.</abstract>
		<language>en</language>
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