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		<site>sibgrapi.sid.inpe.br 802</site>
		<citationkey>NevesBoreGonz:2000:TaSeBo</citationkey>
		<author>Neves, Evelina Maria de Almeida,</author>
		<author>Borelli, Joćo Eduardo,</author>
		<author>Gonzaga, Adilson,</author>
		<title>Target search by bottom-up and top-down fuzzy information</title>
		<conferencename>Brazilian Symposium on Computer Graphics and Image Processing, 13 (SIBGRAPI)</conferencename>
		<year>2000</year>
		<editor>Carvalho, Paulo Cezar Pinto,</editor>
		<editor>Walter, Marcelo,</editor>
		<date>October</date>
		<publisheraddress>Los Alamitos</publisheraddress>
		<publisher>IEEE Computer Society</publisher>
		<conferencelocation>Gramado, RS, Brazil</conferencelocation>
		<keywords>visual perception, target search, bottom-up fuzzy information, top-down fuzzy information, feature extraction, visual field, human visual attention, fuzzy net, fuzzy subsystems, decision rules, visual perception, Gestalt laws, salience index, geometrical objects.</keywords>
		<abstract>One of the basic tasks assigned to the attentional mechanism is to decide which location in the visual field we must pay attention first. An object containing a distinctive feature can attract attention in a bottom-up way. By comparing one object with the others present in the scene, bottom-up conspicuity features are used to guide attention to the most different object. Top-down hints are based on the previous knowledge about the objects or on which features are important to locate them and also have a large influence on the attended locations. Inspired by the mechanisms of human visual attention we developed a new methodology to integrate bottom-up and top-down information by using a fuzzy net containing three fuzzy subsystems. The first bottom-up subsystem allow us to combine features and infer with great flexibility some intuitive decision rules based on the visual perception principles such as the Gestalt laws. The second top-down subsystem combines different features according to the relevance of them in different tasks. Finally, the last subsystem integrates the information of the previous systems and gives a general salience index. The new methodology was tested in geometrical objects considering the features that attract attention to human beings.</abstract>
		<pages>60-66</pages>
		<notes>The conference was held in Gramado, RS, Brazil, from October 17 to 20.</notes>
		<organization>SBC - Brazilian Computer Society</organization>
		<tertiarytype>Full Paper</tertiarytype>
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		<targetfile>60-66.pdf</targetfile>
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