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		<citationkey>VieiraChieFerrGonz:2012:ImMiAn</citationkey>
		<title>Image micro-pattern analysis using Fuzzy Numbers</title>
		<format>DVD, On-line.</format>
		<year>2012</year>
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		<size>828 KiB</size>
		<author>Vieira, Raissa Tavares,</author>
		<author>Chierici, Carlos Eduardo de Oliveira,</author>
		<author>Ferraz, Carolina Toledo,</author>
		<author>Gonzaga, Adilson,</author>
		<affiliation>University of São Paulo</affiliation>
		<affiliation>University of São Paulo</affiliation>
		<affiliation>University of São Paulo</affiliation>
		<affiliation>University of São Paulo</affiliation>
		<editor>Freitas, Carla Maria Dal Sasso,</editor>
		<editor>Sarkar, Sudeep,</editor>
		<editor>Scopigno, Roberto,</editor>
		<editor>Silva, Luciano,</editor>
		<e-mailaddress>caroltoledoferraz@gmail.com</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 25 (SIBGRAPI)</conferencename>
		<conferencelocation>Ouro Preto</conferencelocation>
		<date>Aug. 22-25, 2012</date>
		<booktitle>Proceedings</booktitle>
		<publisher>IEEE Computer Society</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<tertiarytype>Full Paper</tertiarytype>
		<transferableflag>1</transferableflag>
		<contenttype>External Contribution</contenttype>
		<keywords>micro-pattern analysis, fuzzy numbers, texture analysis.</keywords>
		<abstract>This paper proposes a new methodology for micropattern analysis in digital images based on fuzzy numbers. A micro-pattern is the structure of the gray-level pixels within a neighborhood and can describe the spatial context of the image, such as edge, line, spot, blob, corner, texture, and more complex patterns. By treating a pixel neighborhood as a fuzzy set and each pixel gray-level as a fuzzy number, we can evaluate the membership degree of the central pixel to the others. We have called this method the Local Fuzzy Pattern (LFP). Using a sigmoid membership function, we proved that the proposed methodology surpasses the Hit-rate of the Local Binary Pattern (LBP), for texture classification. The LFP proved to be robust to image rotation. Moreover, our proposed formulation for the LFP is a generalization of previously published techniques, such as Texture Unit, LBP, FUNED, and Census Transform.</abstract>
		<language>en</language>
		<targetfile>Image micro-pattern analysis using Fuzzy Numbers.pdf</targetfile>
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