Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/990
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dc.contributorDepartment of Civil and Environmental Engineering-
dc.creatorHuang, ZK-
dc.creatorChau, KW-
dc.date.accessioned2014-12-11T08:25:26Z-
dc.date.available2014-12-11T08:25:26Z-
dc.identifier.issn0096-3003-
dc.identifier.urihttp://hdl.handle.net/10397/990-
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.rightsApplied Mathematics and Computation © 2008 Published by Elsevier Inc. The journal web site is located at http://www.sciencedirect.com.en_US
dc.subjectHistogramen_US
dc.subjectOptimizationen_US
dc.subjectThresholdingen_US
dc.titleA new image thresholding method based on Gaussian mixture modelen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage899-
dc.identifier.epage907-
dc.identifier.volume205-
dc.identifier.issue2-
dc.identifier.doi10.1016/j.amc.2008.05.130-
dcterms.abstractIn this paper, an efficient approach to search for the global threshold of image using Gaussian mixture model is proposed. Firstly, a gray-level histogram of an image is represented as a function of the frequencies of gray-level. Then to fit the Gaussian mixtures to the histogram of image, the expectation maximization (EM) algorithm is developed to estimate the number of Gaussian mixture of such histograms and their corresponding parameterization. Finally, the optimal threshold which is the average of these Gaussian mixture means is chosen. And the experimental results show that the new algorithm performs better.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationApplied mathematics and computation, 15 Nov. 2008, v. 205, no. 2, p. 899-907-
dcterms.isPartOfApplied mathematics and computation-
dcterms.issued2008-11-15-
dc.identifier.isiWOS:000260370000042-
dc.identifier.scopus2-s2.0-54249106794-
dc.identifier.eissn1873-5649-
dc.identifier.rosgroupidr42326-
dc.description.ros2008-2009 > Academic research: refereed > Publication in refereed journal-
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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