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Conference Paper: Object-space multiphase implicit functions

TitleObject-space multiphase implicit functions
Authors
KeywordsImplicit Surfaces
Non-Manifold Surfaces
Support Vector Machines
Linear Programming
Piecewise Polynomial Surfaces
Issue Date2012
PublisherAssociation for Computing Machinery, Inc.. The Journal's web site is located at http://tog.acm.org
Citation
SIGGRAPH 2012. In ACM Transactions on Graphics, 2012, v. 31 n. 4, article no.114, p. 114:1-114:10 How to Cite?
AbstractImplicit functions have a wide range of applications in entertainment, engineering and medical imaging. A standard two-phase implicit function only represents the interior and exterior of a single object. To facilitate solid modeling of heterogeneous objects with multiple internal regions, object-space multiphase implicit functions are much desired. Multiphase implicit functions have much potential in modeling natural organisms, heterogeneous mechanical parts and anatomical atlases. In this paper, we introduce a novel class of object-space multiphase implicit functions that are capable of accurately and compactly representing objects with multiple internal regions. Our proposed multiphase implicit functions facilitate true object-space geometric modeling of heterogeneous objects with non-manifold features. We present multiple methods to create object-space multiphase implicit functions from existing data, including meshes and segmented medical images. Our algorithms are inspired by machine learning algorithms for training multicategory max-margin classifiers. Comparisons demonstrate that our method achieves an error rate one order of magnitude smaller than alternative techniques.
Persistent Identifierhttp://hdl.handle.net/10722/165852
ISSN
2015 Impact Factor: 4.218
2015 SCImago Journal Rankings: 2.552
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorYuan, Zen_US
dc.contributor.authorYu, Yen_US
dc.contributor.authorWang, WPen_US
dc.date.accessioned2012-09-20T08:24:33Z-
dc.date.available2012-09-20T08:24:33Z-
dc.date.issued2012en_US
dc.identifier.citationSIGGRAPH 2012. In ACM Transactions on Graphics, 2012, v. 31 n. 4, article no.114, p. 114:1-114:10en_US
dc.identifier.issn0730-0301-
dc.identifier.urihttp://hdl.handle.net/10722/165852-
dc.description.abstractImplicit functions have a wide range of applications in entertainment, engineering and medical imaging. A standard two-phase implicit function only represents the interior and exterior of a single object. To facilitate solid modeling of heterogeneous objects with multiple internal regions, object-space multiphase implicit functions are much desired. Multiphase implicit functions have much potential in modeling natural organisms, heterogeneous mechanical parts and anatomical atlases. In this paper, we introduce a novel class of object-space multiphase implicit functions that are capable of accurately and compactly representing objects with multiple internal regions. Our proposed multiphase implicit functions facilitate true object-space geometric modeling of heterogeneous objects with non-manifold features. We present multiple methods to create object-space multiphase implicit functions from existing data, including meshes and segmented medical images. Our algorithms are inspired by machine learning algorithms for training multicategory max-margin classifiers. Comparisons demonstrate that our method achieves an error rate one order of magnitude smaller than alternative techniques.-
dc.languageengen_US
dc.publisherAssociation for Computing Machinery, Inc.. The Journal's web site is located at http://tog.acm.org-
dc.relation.ispartofACM Transactions on Graphicsen_US
dc.rightsACM Transactions on Graphics. Copyright © Association for Computing Machinery, Inc..-
dc.subjectImplicit Surfaces-
dc.subjectNon-Manifold Surfaces-
dc.subjectSupport Vector Machines-
dc.subjectLinear Programming-
dc.subjectPiecewise Polynomial Surfaces-
dc.titleObject-space multiphase implicit functionsen_US
dc.typeConference_Paperen_US
dc.identifier.emailYu, Y: yzyu@cs.hku.hken_US
dc.identifier.emailWang, WP: wenping@cs.hku.hken_US
dc.identifier.authorityYu, Y=rp01415en_US
dc.identifier.authorityWang, WP=rp00186en_US
dc.identifier.doi10.1145/2185520.2185610-
dc.identifier.scopuseid_2-s2.0-84872228973-
dc.identifier.hkuros208986en_US
dc.identifier.volume31en_US
dc.identifier.issue4-
dc.identifier.spage114:1-
dc.identifier.epage114:10-
dc.identifier.eissn1557-7368-
dc.identifier.isiWOS:000308250300090-
dc.publisher.placeUnited States-

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