Image histogram thresholding using Gaussian kernel density estimation (English)

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buir.contributor.authorÇetin, A. Enis
buir.contributor.orcidÇetin, A. Enis|0000-0002-3449-1958
dc.contributor.authorSuhre, Alexanderen_US
dc.contributor.authorÇetin, A. Enisen_US
dc.coverage.spatialHaspolat, Turkeyen_US
dc.date.accessioned2016-02-08T12:07:46Z
dc.date.available2016-02-08T12:07:46Z
dc.date.issued2013en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.descriptionDate of Conference: 24-26 April 2013en_US
dc.description.abstractIn this article, image histogram thresholding is carried out using the likelihood of a mixture of Gaussians. In the proposed approach, a prob ability density function (PDF) of the histogram is computed using Gaussian kernel density estimation in an iterative manner. The threshold is found by iteratively computing a mixture of Gaussians for the two clusters. This process is aborted when the current bin is assigned to a different cluster than its predecessor. The method does not envolve an exhaustive search. Visual examples of our segmentation versus Otsu's thresholding method are presented. © 2013 IEEE.en_US
dc.identifier.doi10.1109/SIU.2013.6531341en_US
dc.identifier.urihttp://hdl.handle.net/11693/27992
dc.language.isoTurkishen_US
dc.publisherIEEEen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/SIU.2013.6531341en_US
dc.source.title2013 21st Signal Processing and Communications Applications Conference (SIU)en_US
dc.subjectGaussian kernelen_US
dc.subjectKdeen_US
dc.subjectLmage processingen_US
dc.subjectThresholdingen_US
dc.subjectGaussian kernelsen_US
dc.subjectImage histogramsen_US
dc.subjectKdeen_US
dc.subjectMixture of Gaussiansen_US
dc.subjectThresholdingen_US
dc.subjectThresholding methodsen_US
dc.subjectGaussian distributionen_US
dc.subjectIterative methodsen_US
dc.subjectMixturesen_US
dc.subjectSignal processingen_US
dc.subjectGraphic methodsen_US
dc.titleImage histogram thresholding using Gaussian kernel density estimation (English)en_US
dc.title.alternativeGauss olabilirlik degerlerine dayali goruntu histogrami esiklemeen_US
dc.typeConference Paperen_US

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