Robust region detection via consensus segmentation of deformable shapes

Rodola E, Bulo SR, Cremers D (2014)

Publication Type: Conference contribution

Publication year: 2014

Publisher: Eurographics Association

Book Volume: 33

Pages Range: 97-106

Conference Proceedings Title: Eurographics Symposium on Geometry Processing

DOI: 10.1111/cgf.12435

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.

Involved external institutions

Technische Universität München (TUM) DE Germany (DE) Fondazione Bruno Kessler (FBK) (früher: ITC-irst) IT Italy (IT)

How to cite

APA:

Rodola, E., Bulo, S.R., & Cremers, D. (2014). Robust region detection via consensus segmentation of deformable shapes. In Eurographics Symposium on Geometry Processing (pp. 97-106). Eurographics Association.

MLA:

Rodola, Emanuele, S. Rota Bulo, and D. Cremers. "Robust region detection via consensus segmentation of deformable shapes." Proceedings of the Eurographics Symposium on Geometry Processing Eurographics Association, 2014. 97-106.

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