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dc.contributor.author
Syrkina, Ekaterina
dc.contributor.author
Gonzalez Ballester, Miguel A.
dc.contributor.author
Székely, Gábor
dc.date.accessioned
2020-07-13T12:55:30Z
dc.date.available
2017-06-08T16:55:29Z
dc.date.available
2020-07-13T12:55:30Z
dc.date.issued
2007
dc.identifier.isbn
978-1-4244-1631-8
en_US
dc.identifier.isbn
978-1-4244-1630-1
en_US
dc.identifier.other
10.1109/ICCV.2007.4409162
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/5201
dc.description.abstract
Correspondence establishment is a key step in statistical shape model building. There are several automated methods for solving this problem in 3D, but they usually can only handle objects with simple topology, like that of a sphere or a disc. We propose an extension to correspondence establishment over a population based on the optimization of the minimal description length function, allowing considering objects with arbitrary topology. Instead of using a fixed structure of kernel placement on a sphere for the systematic manipulation of point landmark positions, we rely on an adaptive, hierarchical organization of surface patches. This hierarchy can be built on surfaces of arbitrary topology and the resulting patches are used as a basis for a consistent, multi-scale modification of the surfaces' parameterization, based on point distribution models. The feasibility of the approach is demonstrated on synthetic models with different topologies. ©2007 IEEE.
en_US
dc.language.iso
en
en_US
dc.publisher
IEEE
en_US
dc.title
Correspondence Establishment in Statistical Modeling of Shapes with Arbitrary Topology
en_US
dc.type
Conference Paper
dc.date.published
2007-12-26
ethz.book.title
2007 IEEE 11th International Conference on Computer Vision
en_US
ethz.journal.volume
6
en_US
ethz.pages.start
2604
en_US
ethz.pages.end
2610
en_US
ethz.event
2007 IEEE 11th International Conference on Computer Vision (ICCV 2007)
en_US
ethz.event.location
Rio de Janeiro, Brazil
en_US
ethz.event.date
October 14-21, 2007
en_US
ethz.publication.place
Piscataway, NJ
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich, direkt::00012 - Lehre und Forschung, direkt::00007 - Departemente, direkt::02140 - Departement Informationstechnologie und Elektrotechnik / Department of Information Technology and Electrical Engineering::02652 - Institut für Bildverarbeitung / Computer Vision Laboratory::03633 - Székely, Gábor (emeritus)
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich, direkt::00012 - Lehre und Forschung, direkt::00007 - Departemente, direkt::02140 - Departement Informationstechnologie und Elektrotechnik / Department of Information Technology and Electrical Engineering::02652 - Institut für Bildverarbeitung / Computer Vision Laboratory::03633 - Székely, Gábor (emeritus)
ethz.date.deposited
2017-06-08T16:55:55Z
ethz.source
ECIT
ethz.identifier.importid
imp59364b88d325258903
ethz.ecitpid
pub:15430
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2017-07-12T17:36:40Z
ethz.rosetta.lastUpdated
2020-07-13T12:55:40Z
ethz.rosetta.versionExported
true
ethz.COinS
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