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Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPBW34M/3C9K7LE
Repositorysid.inpe.br/sibgrapi/2012/07.13.18.20
Last Update2012:07.13.18.20.02 caroltoledoferraz@gmail.com
Metadatasid.inpe.br/sibgrapi/2012/07.13.18.20.02
Metadata Last Update2020:02.19.02.18.28 administrator
Citation KeyVieiraChieFerrGonz:2012:ImMiAn
TitleImage micro-pattern analysis using Fuzzy Numbers
FormatDVD, On-line.
Year2012
Access Date2021, Jan. 27
Number of Files1
Size828 KiB
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Author1 Vieira, Raissa Tavares
2 Chierici, Carlos Eduardo de Oliveira
3 Ferraz, Carolina Toledo
4 Gonzaga, Adilson
Affiliation1 University of São Paulo
2 University of São Paulo
3 University of São Paulo
4 University of São Paulo
EditorFreitas, Carla Maria Dal Sasso
Sarkar, Sudeep
Scopigno, Roberto
Silva, Luciano
e-Mail Addresscaroltoledoferraz@gmail.com
Conference NameConference on Graphics, Patterns and Images, 25 (SIBGRAPI)
Conference LocationOuro Preto
DateAug. 22-25, 2012
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2012-09-20 16:45:34 :: caroltoledoferraz@gmail.com -> administrator :: 2012
2020-02-19 02:18:28 :: administrator -> :: 2012
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Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Keywordsmicro-pattern analysis, fuzzy numbers, texture analysis.
AbstractThis 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.
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data URLhttp://urlib.net/rep/8JMKD3MGPBW34M/3C9K7LE
zipped data URLhttp://urlib.net/zip/8JMKD3MGPBW34M/3C9K7LE
Languageen
Target FileImage micro-pattern analysis using Fuzzy Numbers.pdf
User Groupcaroltoledoferraz@gmail.com
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Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
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