Please use this identifier to cite or link to this item: https://doi.org/10.1016/S0167-8655(00)00106-9
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dc.titleColor image segmentation and parameter estimation in a markovian framework
dc.contributor.authorKato, Z.
dc.contributor.authorPong, T.-C.
dc.contributor.authorChung-Mong Lee, J.
dc.date.accessioned2013-07-04T07:30:54Z
dc.date.available2013-07-04T07:30:54Z
dc.date.issued2001
dc.identifier.citationKato, Z., Pong, T.-C., Chung-Mong Lee, J. (2001). Color image segmentation and parameter estimation in a markovian framework. Pattern Recognition Letters 22 (3-4) : 309-321. ScholarBank@NUS Repository. https://doi.org/10.1016/S0167-8655(00)00106-9
dc.identifier.issn01678655
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/38965
dc.description.abstractAn unsupervised color image segmentation algorithm is presented, using a Markov random field (MRF) pixel classification model. We propose a new method to estimate initial mean vectors effectively even if the histogram does not have clearly distinguishable peaks. The only parameter supplied by the user is the number of classes. © 2001 Elsevier Science B.V. All rights reserved.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/S0167-8655(00)00106-9
dc.sourceScopus
dc.subjectColor
dc.subjectMarkov random field
dc.subjectParameter estimation
dc.subjectPixel classification
dc.subjectUnsupervised image segmentation
dc.typeArticle
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1016/S0167-8655(00)00106-9
dc.description.sourcetitlePattern Recognition Letters
dc.description.volume22
dc.description.issue3-4
dc.description.page309-321
dc.identifier.isiut000167983900005
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