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dc.contributor.authorDing, Yuhua
dc.contributor.authorVuchtsevunos, George J.
dc.contributor.authorYezzi, Anthony
dc.contributor.authorDaley, Wayne
dc.contributor.authorHeck-Ferri, Bonnie S.
dc.date.accessioned2013-12-03T17:49:23Z
dc.date.available2013-12-03T17:49:23Z
dc.date.issued2003-04
dc.identifier.citationDing, Y.; Vachtsevanos, G.J.; Yezzi, A.J.; Daley, W.; & Heck-Ferri, B.S. (2003). “A Real-Time Curve Evolution-Based Image Fusion Algorithm for Multisensory Image Segmentation”. Proceedings of the 2003 International Conference on Multimedia and Expo (ICME '03), Vol. 1, July 2003, pp.I,369-72.en_US
dc.identifier.isbn0-7803-7965-9 (ICME 2003)
dc.identifier.urihttp://hdl.handle.net/1853/49753
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.descriptionThis paper was originally published in the Proceedings of the 2003 IEEE lntemational Conference on Acoustics, Speech, & Signal Processing, April 6-10, 2003, Hong Kong (cancelled). Reprinted with permission.
dc.descriptionDOI: 10.1109/ICME.2003.1220931
dc.description.abstractA partial differential equation (PDE)-based feature-level image fusion approach is proposed for multisensory image segmentation. The energy functional of the proposed fusion model is a weighted sum of several functionals, each constructed based on the characteristics of the sensor image. The weight selection decides the way that the model handles redundant, conflicting, or complementary information involved in the multisensory data. The method is implemented using level sets and is fast enough for real-time segmentation tasks. Finally the algorithm is applied to the segmentation of x-ray and visual images, and the results show that the fusion algorithm is efficient, accurate, and robust.en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectFusionen_US
dc.subjectLevel setsen_US
dc.subjectMultisensory image segmentationen_US
dc.subjectPartial differential equationen_US
dc.subjectSensor imageen_US
dc.subjectVisual imageen_US
dc.subjectX-ray imageen_US
dc.titleA Real-Time Curve Evolution-Based Image Fusion Algorithm for Multisensory Image Segmentationen_US
dc.typeProceedingsen_US
dc.contributor.corporatenameGeorgia Institute of Technology. School of Electrical and Computer Engineeringen_US
dc.publisher.originalInstitute of Electrical and Electronics Engineers
dc.identifier.doi10.1109/ICME.2003.1220931
dc.embargo.termsnullen_US


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