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		<citationkey>MeloAnge:2015:ReAuIn</citationkey>
		<title>Reconhecimento Automático do Inseto Diaphorina citri em Imagens de Microscopia</title>
		<format>On-line</format>
		<year>2015</year>
		<numberoffiles>1</numberoffiles>
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		<author>Melo, José Leonardo dos Santos,</author>
		<author>Angelo, Michele Fúlvia,</author>
		<affiliation>PGCA - Pós-Graduação em Computação Aplicada, UEFS - Universidade Estadual de Feira de Santana</affiliation>
		<affiliation>PGCA - Pós-Graduação em Computação Aplicada, UEFS - Universidade Estadual de Feira de Santana</affiliation>
		<editor>Rios, Ricardo Araujo,</editor>
		<editor>Paiva, Afonso,</editor>
		<e-mailaddress>leomelocomputacao@gmail.com</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 28 (SIBGRAPI)</conferencename>
		<conferencelocation>Salvador</conferencelocation>
		<date>Aug. 26-29, 2015</date>
		<publisher>Sociedade Brasileira de Computação</publisher>
		<publisheraddress>Porto Alegre</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Work in Progress</tertiarytype>
		<transferableflag>1</transferableflag>
		<keywords>Citriculture, Yellow Sticky Traps, Diaphorina citri, Huanglongbing (HLB), Classification of Digital Images, Machine Learning.</keywords>
		<abstract>This paper presents a proposal for the use of computational approaches to image classification of insect Diaphorina citri, in microscopy, distinguishing it from other insects commonly found in citrus regions of Sao Paulo, Brazil. In addition, comparisons will be made between the approaches used and optimization of the proposed classifiers. Extractors local features invariant to rotation and scale will be used along with different approaches bag-of-features and will be classified as machine learning algorithms. This work is a significant step to enable the creation of computational systems that automate the important process of counting these insects.</abstract>
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