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dc.contributor.authorRak, Markoen_US
dc.contributor.authorEngel, Karinen_US
dc.contributor.authorTönnies, Klaus D.en_US
dc.contributor.editorMichael Bronstein and Jean Favre and Kai Hormannen_US
dc.description.abstractIn this work we address part-based object detection under variability of part shapes and spatial relations. Our approach bases on the hierarchical finite element modeling concept of Engel and Tönnies [ET09a, ET09b]. They model object parts by elastic materials, which adapt to image structures via image-derived forces. Spatial part relations are realized through additional layers of elastic material forming an elastic hierarchy. We present a closed-form solution to this concept, reformulating the hierarchical optimization problem into the optimization of a non-hierarchical finite element model. This allows us to apply standard finite element techniques to hierarchical problems and to provide an efficient framework for part-based object detection. We demonstrate our approach at the example of lumbar column detection in magnetic resonance imaging on a data set of 49 subjects. Given a rough model initialization, our approach solved the detection problem reliably in 45 out of 49 cases, showing computation times of only a few seconds per subject.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectI.4.8 [Computer Graphics]en_US
dc.subjectScene Analysisen_US
dc.subjectObject recognitionen_US
dc.subjectI.4.8 [Computer Graphics]en_US
dc.subjectScene Analysisen_US
dc.titleClosed-Form Hierarchical Finite Element Models for Part-Based Object Detectionen_US
dc.description.seriesinformationVision, Modeling & Visualizationen_US

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  • VMV13
    ISBN 978-3-905674-51-4

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