Globally optimal joint image segmentation and shape matching based on Wasserstein modes
A functional for joint variational object segmentation and shape matching is developed. The formulation is based on optimal transport w.r.t. geometric distance and local feature similarity. Geometric invariance and modelling of object-typical statistical variations is achieved by introducing degrees...
Gespeichert in:
| Hauptverfasser: | , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2015
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| In: |
Journal of mathematical imaging and vision
Year: 2014, Jahrgang: 52, Heft: 3, Pages: 436-458 |
| ISSN: | 1573-7683 |
| DOI: | 10.1007/s10851-014-0546-8 |
| Online-Zugang: | Resolving-System, Volltext: http://dx.doi.org/10.1007/s10851-014-0546-8 Verlag, Volltext: https://link.springer.com/article/10.1007/s10851-014-0546-8 
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| Verfasserangaben: | Bernhard Schmitzer, Christoph Schnörr |
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Globally optimal joint image segmentation and shape matching based on Wasserstein modes
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