Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/38578
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dc.coverage.spatialCertain analysis of interval type 2 Fuzzy logic for image Processing applicationsen_US
dc.date.accessioned2015-04-01T11:32:30Z-
dc.date.available2015-04-01T11:32:30Z-
dc.date.issued2015-04-01-
dc.identifier.urihttp://hdl.handle.net/10603/38578-
dc.description.abstractDomain knowledge of real life problems are often uncertain newlineimprecise and vague therefore that creates difficulty in decision making newlinewhile solving by conventional approaches Among the various methods of newlinehandling uncertainties fuzzy image processing has received considerable newlineattention in the literature for several decades Fuzzy logic FL explores newlinehuman reasoning power using linguistic terms which are modeled as Type 1 newlineFuzzy Sets T1FSs and represented by Membership Functions MFs newlineHowever the MFs of T1FSs are crisp and cannot tackle all kinds of newlineUncertainties Introducing Type 2 Fuzzy Logic T2FL an extension of newlineType 1 Fuzzy Logic simplifies the problems where the MFs are themselves newlinefuzzy with three dimensional representations newlineA number of papers that exist in the literature claim that the newlineperformance of Type 2 Fuzzy Logic System T2FLS is better than Type 1 newlineFuzzy Logic System T1FLS under various conditions environments newlineInterval Type 2 Fuzzy Logic IT2FL is the special case of T2FL The newlineresearchers have shown that the computational cost is high in T2FLS Hence newlineIT2FLS is more commonly seen in the literature due to the fact that the newlinecomputations are more manageable In this dissertation the research activities newlineare focused on the IT2FLS Basically image processing encounters newlineuncertainty and imprecision e g to determine whether a pixel is an edgepixel newlineor not or whether a pixel is contaminated with noise or not Another newlineexample concerns similarity measures which measure the degree of similarity newlinebetween two images which are similar to each other newlineen_US
dc.format.extentxxviii, 230p.en_US
dc.languageEnglishen_US
dc.relationp208-228.en_US
dc.rightsuniversityen_US
dc.titleCertain analysis of interval type 2 Fuzzy logic for image Processing applicationsen_US
dc.title.alternativeen_US
dc.creator.researcherMurugeswari Pen_US
dc.subject.keywordInterval Type 2 Fuzzy Logicen_US
dc.subject.keywordType 1 Fuzzy Setsen_US
dc.subject.keywordType 2 Fuzzy Logic Systemen_US
dc.description.notereference p208-228.en_US
dc.contributor.guideManimegalai Den_US
dc.publisher.placeChennaien_US
dc.publisher.universityAnna Universityen_US
dc.publisher.institutionFaculty of Information and Communication Engineeringen_US
dc.date.registeredn.d,en_US
dc.date.completed01/05/2014en_US
dc.date.awarded30/05/2014en_US
dc.format.dimensions23cm.en_US
dc.format.accompanyingmaterialNoneen_US
dc.source.universityUniversityen_US
dc.type.degreePh.D.en_US
Appears in Departments:Faculty of Information and Communication Engineering

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04_acknowledgement.pdf21.52 kBAdobe PDFView/Open
05_content.pdf170.99 kBAdobe PDFView/Open
06_chapter1.pdf69.95 kBAdobe PDFView/Open
07_chapter2.pdf813.13 kBAdobe PDFView/Open
08_chapter3.pdf113.21 kBAdobe PDFView/Open
09_chapter4.pdf947.06 kBAdobe PDFView/Open
10_chapter5.pdf1.36 MBAdobe PDFView/Open
11_chapter6.pdf1.08 MBAdobe PDFView/Open
12_chapter7.pdf549.22 kBAdobe PDFView/Open
13_chapter8.pdf444.97 kBAdobe PDFView/Open
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15_reference.pdf99.36 kBAdobe PDFView/Open
16_publication.pdf20.2 kBAdobe PDFView/Open
17_vitae.pdf18.46 kBAdobe PDFView/Open


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