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dc.contributor.author Zhu, Yanong
dc.contributor.author Williams, Stuart
dc.contributor.author Zwiggelaar, Reyer
dc.date.accessioned 2008-03-04T14:25:48Z
dc.date.available 2008-03-04T14:25:48Z
dc.date.issued 2007
dc.identifier.citation Zhu , Y , Williams , S & Zwiggelaar , R 2007 , ' A hybrid ASM approach for sparse volumetric data segmentation ' Pattern Recognition and Image Analysis , vol 17 , no. 2 , pp. 252-258 . , 10.1134/S1054661807020125 en
dc.identifier.issn 1054-6618
dc.identifier.other PURE: 75824
dc.identifier.other dspace: 2160/521
dc.identifier.uri http://hdl.handle.net/2160/521
dc.identifier.uri http://www.springerlink.com/content/f202210r227853j7/ en
dc.identifier.uri http://www.ingentaconnect.com/content/maik/11493/2007/00000017/00000002/00002012 en
dc.description Y. Zhu, S. Williams and R. Zwiggelaar, 'A hybrid ASM approach for sparse volumetric data segmentation', Pattern Recognition and Image Analysis 17 (2), 252-258 (2007) en
dc.description.abstract Three-Dimensional (3D) Active Shape Modeling (ASM) is a straightforward extension of 2D ASM. 3D ASM is robust when true volumetric data is considered. However, when the information in one dimension is sparse, pure 3D ASM tends to be less robust. We present a hybrid 2D + 3D methodology which can deal with sparse 3D data. 2D and 3D ASMs are combined to obtain a 'global optimal' segmentation of the 3D object embedded in the data set, rather than the 'locally optimal' segmentation on separate slices. Experimental results indicate that the developed approach shows equivalent precision on separate slices but higher consistency for whole volumes when compared to 2D ASM, while the results for whole volumes are improved when compared to the pure 3D ASM approach. en
dc.format.extent 7 en
dc.language.iso eng
dc.relation.ispartof Pattern Recognition and Image Analysis en
dc.title A hybrid ASM approach for sparse volumetric data segmentation en
dc.type Text en
dc.type.publicationtype Article (Journal) en
dc.identifier.doi http://dx.doi.org/10.1134/S1054661807020125
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Vision, Graphics and Visualisation Group en
dc.description.status Peer reviewed en


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