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		<citationkey>VargasPaesVasc:2016:EsSoRe</citationkey>
		<title>Um Estudo sobre Redes Neurais Convolucionais e sua Aplicação em Detecção de Pedestres</title>
		<format>On-line</format>
		<year>2016</year>
		<numberoffiles>1</numberoffiles>
		<size>397 KiB</size>
		<author>Vargas, Ana Caroline Gomes,</author>
		<author>Paes, Aline,</author>
		<author>Vasconcelos, Cristina Nader,</author>
		<affiliation>UFF</affiliation>
		<affiliation>UFF</affiliation>
		<affiliation>UFF</affiliation>
		<editor>Aliaga, Daniel G.,</editor>
		<editor>Davis, Larry S.,</editor>
		<editor>Farias, Ricardo C.,</editor>
		<editor>Fernandes, Leandro A. F.,</editor>
		<editor>Gibson, Stuart J.,</editor>
		<editor>Giraldi, Gilson A.,</editor>
		<editor>Gois, João Paulo,</editor>
		<editor>Maciel, Anderson,</editor>
		<editor>Menotti, David,</editor>
		<editor>Miranda, Paulo A. V.,</editor>
		<editor>Musse, Soraia,</editor>
		<editor>Namikawa, Laercio,</editor>
		<editor>Pamplona, Mauricio,</editor>
		<editor>Papa, João Paulo,</editor>
		<editor>Santos, Jefersson dos,</editor>
		<editor>Schwartz, William Robson,</editor>
		<editor>Thomaz, Carlos E.,</editor>
		<e-mailaddress>carol.gomes.vargas@gmail.com</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 29 (SIBGRAPI)</conferencename>
		<conferencelocation>São José dos Campos, SP, Brazil</conferencelocation>
		<date>4-7 Oct. 2016</date>
		<publisher>Sociedade Brasileira de Computação</publisher>
		<publisheraddress>Porto Alegre</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Undergraduate Work</tertiarytype>
		<transferableflag>1</transferableflag>
		<keywords>Pedestrian Detection, Computer Vision, Machine Learning, CNN, SVM, HOG, Adaboost, Haar features.</keywords>
		<abstract>This work addresses Deep Learning from the point of view of the Computer Vision area, drawing a parallel between the two areas, considering the pedestrian detection task. To achieve that, the experimental results of two classical Computer Vision approaches are compared to the results obtained from a Convolutional Neural Network, which is known for obtaining the state of the art to the problem of pedestrian detection.</abstract>
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