Parametric-based Reconstruction Of 3D Mesh Models; Towards the Generation of a Parametric Human Foot Biomodel

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dc.contributor.authorAgathos, Alexanderen_US
dc.contributor.authorAzariadis, Philipen_US
dc.contributor.editorKatja Bühler and Lars Linsen and Nigel W. Johnen_US
dc.date.accessioned2015-09-14T04:49:11Z
dc.date.available2015-09-14T04:49:11Z
dc.date.issued2015en_US
dc.description.abstractIn this work a new reconstruction technique is presented based on Parametric-Based Deformation. The new method uses a template 3D mesh model which is deformed according to user-defined semantic parameters in order to derive a new 3D object. The proposed methodology is divided in three stages: template construction (production of a template mesh model), regression (the template model is deformed to match sample models) and prediction (new instance mesh models are derived based on user parameters). The proposed method has been successfully applied for the reconstruction of human and animal skeletal models. A practical and novel reconstruction methodology has been developed and implemented for the reconstruction of the human foot given a bi-planar X-ray. The purpose of this methodology is to support the parametric generation of human foot biomodels in an automated way. The generated biomodels are employed by simulation tools based on Finite Element Analysis to test the stress factors that the foot is undertaking during its contact with the ground or a footwear sole structure.en_US
dc.description.sectionheadersPostersen_US
dc.description.seriesinformationEurographics Workshop on Visual Computing for Biology and Medicineen_US
dc.identifier.doi10.2312/vcbm.20151232en_US
dc.identifier.isbn978-3-905674-82-8en_US
dc.identifier.issn2070-5786en_US
dc.identifier.pages221-222en_US
dc.identifier.urihttps://doi.org/10.2312/vcbm.20151232en_US
dc.publisherThe Eurographics Associationen_US
dc.titleParametric-based Reconstruction Of 3D Mesh Models; Towards the Generation of a Parametric Human Foot Biomodelen_US
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VCBM 15: Eurographics Workshop on Visual Computing for Biology and Medicine