Robust Region Detection via Consensus Segmentation of Deformable Shapes

Simple item page
dc.contributor.authorRodolĂ , Emanueleen_US
dc.contributor.authorBulò, Samuel Rotaen_US
dc.contributor.authorCremers, Danielen_US
dc.contributor.editorThomas Funkhouser and Shi-Min Huen_US
dc.date.accessioned2015-03-03T12:41:45Z
dc.date.available2015-03-03T12:41:45Z
dc.date.issued2014en_US
dc.description.abstractWe consider the problem of stable region detection and segmentation of deformable shapes. We pursue this goal by determining a consensus segmentation from a heterogeneous ensemble of putative segmentations, which are generated by a clustering process on an intrinsic embedding of the shape. The intuition is that the consensus segmentation, which relies on aggregate statistics gathered from the segmentations in the ensemble, can reveal components in the shape that are more stable to deformations than the single baseline segmentations. Compared to the existing approaches, our solution exhibits higher robustness and repeatability throughout a wide spectrum of non-rigid transformations. It is computationally efficient, naturally extendible to point clouds, and remains semantically stable even across different object classes. A quantitative evaluation on standard datasets confirms the potentiality of our method as a valid tool for deformable shape analysis.en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.identifier.issn1467-8659en_US
dc.identifier.urihttps://doi.org/10.1111/cgf.12435en_US
dc.publisherThe Eurographics Association and John Wiley and Sons Ltd.en_US
dc.titleRobust Region Detection via Consensus Segmentation of Deformable Shapesen_US
Files
Collections
33-Issue 5
SGP14: Eurographics Symposium on Geometry Processing (CGF 33-5)