Interval type‐2 fuzzy logic for edges detection in digital images
Identifieur interne : 000957 ( Main/Exploration ); précédent : 000956; suivant : 000958Interval type‐2 fuzzy logic for edges detection in digital images
Auteurs : Olivia Mendoza [Mexique] ; Patricia Melin [Mexique] ; Guillermo Licea [Mexique]Source :
- International Journal of Intelligent Systems [ 0884-8173 ] ; 2009-11.
Abstract
Edges detection in a digital image is the first step in an image recognition system. In this paper, we show an efficient edges detector using an interval type‐2 fuzzy inference system (FIS‐2). The FIS‐2 uses as input the original images after applying Sobel filters and attenuation filters, then the fuzzy rules infer normalized values for the edges images, especially useful to enhance the performance of neural networks. To illustrate the results, we built frequency histograms of some images and compare the results of the FIS‐2 edge's detector with the gradient magnitude method and a type‐1 fuzzy inference system (FIS‐1). The FIS‐2 results are better than the gradient magnitude and FIS‐1, because the edges preserve more detail of the original images, and the backgrounds are more homogeneous than with FIS‐1 and the gradient's magnitude method. © 2009 Wiley Periodicals, Inc.
Url:
DOI: 10.1002/int.20378
Affiliations:
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J. Intell. Syst.</title><idno type="ISSN">0884-8173</idno><idno type="eISSN">1098-111X</idno><imprint><publisher>Wiley Subscription Services, Inc., A Wiley Company</publisher><pubPlace>Hoboken</pubPlace><date type="published" when="2009-11">2009-11</date><biblScope unit="volume">24</biblScope><biblScope unit="issue">11</biblScope><biblScope unit="page" from="1115">1115</biblScope><biblScope unit="page" to="1133">1133</biblScope></imprint><idno type="ISSN">0884-8173</idno></series><idno type="istex">A90A50AD839D7553731BB244856C646606D2342C</idno><idno type="DOI">10.1002/int.20378</idno><idno type="ArticleID">INT20378</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0884-8173</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Edges detection in a digital image is the first step in an image recognition system. In this paper, we show an efficient edges detector using an interval type‐2 fuzzy inference system (FIS‐2). The FIS‐2 uses as input the original images after applying Sobel filters and attenuation filters, then the fuzzy rules infer normalized values for the edges images, especially useful to enhance the performance of neural networks. To illustrate the results, we built frequency histograms of some images and compare the results of the FIS‐2 edge's detector with the gradient magnitude method and a type‐1 fuzzy inference system (FIS‐1). The FIS‐2 results are better than the gradient magnitude and FIS‐1, because the edges preserve more detail of the original images, and the backgrounds are more homogeneous than with FIS‐1 and the gradient's magnitude method. © 2009 Wiley Periodicals, Inc.</div></front></TEI><affiliations><list><country><li>Mexique</li></country></list><tree><country name="Mexique"><noRegion><name sortKey="Mendoza, Olivia" sort="Mendoza, Olivia" uniqKey="Mendoza O" first="Olivia" last="Mendoza">Olivia Mendoza</name></noRegion><name sortKey="Licea, Guillermo" sort="Licea, Guillermo" uniqKey="Licea G" first="Guillermo" last="Licea">Guillermo Licea</name><name sortKey="Melin, Patricia" sort="Melin, Patricia" uniqKey="Melin P" first="Patricia" last="Melin">Patricia Melin</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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