Abstract
The minimal geodesic models based on the Eikonal equations are capable of finding suitable solutions in various image segmentation scenarios. Existing geodesic-based segmentation approaches usually exploit the image features in conjunction with geometric regularization terms (such as curve length or elastica length) for computing geodesic paths. In this paper, we consider a more complicated problem: finding simple and closed geodesic curves which are imposed a convexity shape prior. The proposed approach relies on an orientation-lifting strategy, by which a planar curve can be mapped to an high-dimensional orientation space. The convexity shape prior serves as a constraint for the construction of local metrics. The geodesic curves in the lifted space then can be efficiently computed through the fast marching method. In addition, we introduce a way to incorporate region-based homogeneity features into the proposed geodesic model so as to solve the region-based segmentation issues with shape prior constraints.
Original language | English |
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Title of host publication | Proceedings - 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021 |
Editors | Lisa O’Conner |
Publisher | IEEE |
Pages | 6880-6889 |
Number of pages | 10 |
ISBN (Electronic) | 9781665428125 |
DOIs | |
Publication status | Published - 28 Feb 2022 |
Event | 18th IEEE/CVF International Conference on Computer Vision, ICCV 2021 - Virtual, Online, Canada Duration: 11 Oct 2021 → 17 Oct 2021 https://iccv2021.thecvf.com/home (conference website) |
Publication series
Name | Proceedings of the IEEE International Conference on Computer Vision |
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ISSN (Print) | 1550-5499 |
Conference
Conference | 18th IEEE/CVF International Conference on Computer Vision, ICCV 2021 |
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Country/Territory | Canada |
City | Virtual, Online |
Period | 11/10/21 → 17/10/21 |
Internet address |
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Scopus Subject Areas
- Software
- Computer Vision and Pattern Recognition