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.

Robust region detection via consensus segmentation of deformable shapes / Rodolà, E.; Bulò, S. Rota; Cremers, D.. - In: COMPUTER GRAPHICS FORUM. - ISSN 0167-7055. - 33:5(2014), pp. 97-106. [10.1111/cgf.12435]

Robust region detection via consensus segmentation of deformable shapes

Rodolà, E.;
2014

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.
2014
categories and subject descriptors; computational geometry; shape analysis
01 Pubblicazione su rivista::01a Articolo in rivista
Robust region detection via consensus segmentation of deformable shapes / Rodolà, E.; Bulò, S. Rota; Cremers, D.. - In: COMPUTER GRAPHICS FORUM. - ISSN 0167-7055. - 33:5(2014), pp. 97-106. [10.1111/cgf.12435]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1229170
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