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		<citationkey>SilvaMontHiraHira:2017:ImOpLe</citationkey>
		<title>Image operator learning based on local features</title>
		<format>On-line</format>
		<year>2017</year>
		<numberoffiles>1</numberoffiles>
		<size>754 KiB</size>
		<author>Silva, Augusto César Monteiro,</author>
		<author>Montagner, Igor dos Santos,</author>
		<author>Hirata Jr, Roberto,</author>
		<author>Hirata, Nina Sumiko Tomita,</author>
		<affiliation>Institute of Mathematics and Statistics</affiliation>
		<affiliation>Institute of Mathematics and Statistics</affiliation>
		<affiliation>Institute of Mathematics and Statistics</affiliation>
		<affiliation>Institute of Mathematics and Statistics</affiliation>
		<editor>Torchelsen, Rafael Piccin,</editor>
		<editor>Nascimento, Erickson Rangel do,</editor>
		<editor>Panozzo, Daniele,</editor>
		<editor>Liu, Zicheng,</editor>
		<editor>Farias, Mylène,</editor>
		<editor>Viera, Thales,</editor>
		<editor>Sacht, Leonardo,</editor>
		<editor>Ferreira, Nivan,</editor>
		<editor>Comba, João Luiz Dihl,</editor>
		<editor>Hirata, Nina,</editor>
		<editor>Schiavon Porto, Marcelo,</editor>
		<editor>Vital, Creto,</editor>
		<editor>Pagot, Christian Azambuja,</editor>
		<editor>Petronetto, Fabiano,</editor>
		<editor>Clua, Esteban,</editor>
		<editor>Cardeal, Flávio,</editor>
		<e-mailaddress>augusto.cesar.silva@usp.br</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 30 (SIBGRAPI)</conferencename>
		<conferencelocation>Niterói, RJ, Brazil</conferencelocation>
		<date>17-20 Oct. 2017</date>
		<publisher>Sociedade Brasileira de Computação</publisher>
		<publisheraddress>Porto Alegre</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Undergraduate Work</tertiarytype>
		<transferableflag>1</transferableflag>
		<keywords>morphological operators, local features, image operator learning.</keywords>
		<abstract>Morphological operators in image processing have a wide range of applications, like in medical imaging and document image analysis. The design of such operators are made, mainly, by a trial and error approach. Another method to design these operators consists in using machine learning algorithms to define a local transformation that represents an operator. Previous works used mainly the intensity values of the pixels as feature vectors in the machine learning algorithms. We propose to extract different features, calculated from the image, to create different feature vectors to be used in the machine learning algorithms. We experiment this approach in four different public datasets, and results show that different features have a significant impact on the learned operators, but, just like the operators, the feature that provides better results also depends on the dataset used.</abstract>
		<language>en</language>
		<targetfile>image-operator-learning-camera-ready.pdf</targetfile>
		<usergroup>augusto.cesar.silva@usp.br</usergroup>
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		<citingitemlist>sid.inpe.br/sibgrapi/2017/09.12.13.04 8</citingitemlist>
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