Robust Region Detection via Consensus Segmentation of Deformable Shapes

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 intui...

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Veröffentlicht in:Computer graphics forum Jg. 33; H. 5; S. 97 - 106
Hauptverfasser: Rodolà, E., Bulò, S. Rota, Cremers, D.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Blackwell Publishing Ltd 01.08.2014
Schlagworte:
Analysis
Categories and Subject Descriptors (according to ACM CCS)
Computational efficiency
Computer graphics
Deformation
Formability
I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling-Shape Analysis
Image processing systems
Robustness
Segmentation
Statistics
Studies
Three dimensional models
Topological manifolds
Transformations
ISSN:0167-7055, 1467-8659
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung: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.
Bibliographie:ArticleID:CGF12435
Supporting Information
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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.12435