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• W2130608009 abstract "We 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." @default.
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• W2130608009 date "2014-08-01" @default.
• W2130608009 modified "2023-10-17" @default.
• W2130608009 title "Robust Region Detection via Consensus Segmentation of Deformable Shapes" @default.
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• W2130608009 doi "https://doi.org/10.1111/cgf.12435" @default.
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