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dc.contributor.authorKim, Junmo
dc.contributor.authorFisher, John W., III
dc.contributor.authorÇetin, Müjdat
dc.contributor.authorYezzi, Anthony
dc.contributor.authorWillsky, Alan S.
dc.date.accessioned2013-09-09T17:46:07Z
dc.date.available2013-09-09T17:46:07Z
dc.date.issued2003-09
dc.identifier.citationKim, J.; Fisher, J.W.; Cetin, M.; Yezzi, A., Jr.; & Willsky, A.S. (2003). "Incorporating Complex Statistical Information in Active Contour-Based Image Segmentation". Proceedings of the 2003 International Conference on Image Processing (ICIP 2003), Vol. 2, (September 2003), pp.II-655-8 Vol. 3.en_US
dc.identifier.isbn0-7803-7750-8
dc.identifier.issn1522-4880
dc.identifier.urihttp://hdl.handle.net/1853/48861
dc.description© 2003 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.descriptionPresented at the 2003 IEEE International Conference on Image Processing (ICIP 2003), 14-18 September 2000, Barcelona, Spain.
dc.descriptionDOI: 10.1109/ICIP.2003.1246765
dc.description.abstractAn information-theoretic method for multiphase image segmentation, in an active contour-based framework is proposed. Our approach is based on nonparametric density estimates, and is able to solve problems involving arbitrary probability densities for the region intensities. This is achieved by maximizing the mutual information between the region labels and the image pixel intensities, in order to segment up to 2m regions using m curves. The method does not require any prior training regarding the regions of interest, but rather learns the probability densities during the evolution process. We present some illustrative experimental results, demonstrating the power of the proposed segmentation approach.en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectDensity estimatesen_US
dc.subjectEvolutionen_US
dc.subjectImage segmentationen_US
dc.subjectInformation-theoretic methoden_US
dc.titleIncorporating Complex Statistical Information in Active Contour-Based Image Segmentationen_US
dc.typeProceedingsen_US
dc.contributor.corporatenameGeorgia Institute of Technology. School of Electrical and Computer Engineeringen_US
dc.contributor.corporatenameMassachusetts Institute of Technology. Laboratory for Information and Decision Systemsen_US
dc.publisher.originalInstitute of Electrical and Electronics Engineers
dc.identifier.doi10.1109/ICIP.2003.1246765
dc.embargo.termsnullen_US


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