Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/40816
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dc.coverage.spatialInvestigations on clustering based Image segmentation methods for Multi resolution imagesen_US
dc.date.accessioned2015-05-09T09:17:50Z-
dc.date.available2015-05-09T09:17:50Z-
dc.date.issued2015-05-09-
dc.identifier.urihttp://hdl.handle.net/10603/40816-
dc.description.abstractSatellite images often require segmentation in the presence of newlineUncertainty caused due to factors like environmental conditions poor newlineresolution and poor illumination Specifically in image segmentation newlineproblems the input data involve properties of image pixels sometimes derived newlinefrom very different sources Therefore we need to define different kernel newlinefunctions purposely for the intensity information and the texture information newlineseparately We then combine these kernel functions and apply the composite newlinekernel in Multiple Kernel Fuzzy C Means MKFCM algorithm to obtain newlinebetter image segmentation results newlineThe performance of MKFCM type algorithms depends on the newlineoptimization of segmentation accuracy and its efficiency Based on accuracy newlinethe proposed method is concentrated on obtaining a robust segmentation for newlinenoisy images and a correct detection of small regions based on low threshold newlinevalue The efficiency can be obtained from number of iterations if the newlinenumber of iteration is less the efficiency of the proposed method is good newlineSimulation results obtained for a sample satellite image and low resolution newlinesatellite image requires less number of iterations and segmentation has been newlineachieved for a lesser threshold level newlineen_US
dc.format.extentxix, 189p.en_US
dc.languageEnglishen_US
dc.relationp176-186.en_US
dc.rightsuniversityen_US
dc.titleInvestigations on clustering based Image segmentation methods for Multi resolution imagesen_US
dc.title.alternativeen_US
dc.creator.researcherGanesh Men_US
dc.subject.keywordMultiple Kernel Fuzzy C Meansen_US
dc.subject.keywordMulti resolution imagesen_US
dc.description.notereference p176-186.en_US
dc.contributor.guidePalanisamy Ven_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.completed01/08/2014en_US
dc.date.awarded30/08/2014en_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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01_title.pdfAttached File34.89 kBAdobe PDFView/Open
02_certificate.pdf1.34 MBAdobe PDFView/Open
03_abstract.pdf392.94 kBAdobe PDFView/Open
04_acknowledgement.pdf59.79 kBAdobe PDFView/Open
05_content.pdf476.37 kBAdobe PDFView/Open
06_chapter1.pdf2.58 MBAdobe PDFView/Open
07_chapter2.pdf18.85 MBAdobe PDFView/Open
08_chapter3.pdf6.29 MBAdobe PDFView/Open
09_chapter4.pdf24.06 MBAdobe PDFView/Open
10_chapter5.pdf8.75 MBAdobe PDFView/Open
11_chapter6.pdf370.39 kBAdobe PDFView/Open
12_reference.pdf985.67 kBAdobe PDFView/Open
13_publication.pdf93.63 kBAdobe PDFView/Open
14_vitae.pdf47.87 kBAdobe PDFView/Open


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