Learning Boundary Edges for 3D‐Mesh Segmentation

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dc.contributor.authorBenhabiles, Halimen_US
dc.contributor.authorLavoué, Guillaumeen_US
dc.contributor.authorVandeborre, Jean‐Philippeen_US
dc.contributor.authorDaoudi, Mohameden_US
dc.contributor.editorEduard Groeller and Holly Rushmeieren_US
dc.date.accessioned2015-02-27T16:45:30Z
dc.date.available2015-02-27T16:45:30Z
dc.date.issued2011en_US
dc.description.abstractThis paper presents a 3D‐mesh segmentation algorithm based on a learning approach. A large database of manually segmented 3D‐meshes is used to learn a boundary edge function. The function is learned using a classifier which automatically selects from a pool of geometric features the most relevant ones to detect candidate boundary edges. We propose a processing pipeline that produces smooth closed boundaries using this edge function. This pipeline successively selects a set of candidate boundary contours, closes them and optimizes them using a snake movement. Our algorithm was evaluated quantitatively using two different segmentation benchmarks and was shown to outperform most recent algorithms from the state‐of‐the‐art.en_US
dc.description.number8
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.volume30
dc.identifier.issn1467-8659en_US
dc.identifier.urihttps://doi.org/10.1111/j.1467-8659.2011.01967.xen_US
dc.publisherThe Eurographics Association and Blackwell Publishing Ltd.en_US
dc.titleLearning Boundary Edges for 3D‐Mesh Segmentationen_US
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