A Generalized Asymmetric Dual-front Model for Active Contours and Image
Segmentation
- URL: http://arxiv.org/abs/2006.07839v3
- Date: Tue, 4 May 2021 09:19:18 GMT
- Title: A Generalized Asymmetric Dual-front Model for Active Contours and Image
Segmentation
- Authors: Da Chen, Jack Spencer, Jean-Marie Mirebeau, Ke Chen, Minglei Shu and
Laurent D. Cohen
- Abstract summary: Voronoi diagram-based dual-front active contour models are known as a powerful and efficient way for addressing the image segmentation and domain partitioning problems.
In this paper, we introduce a type of asymmetric quadratic metrics dual-front model.
The proposed dual-front model can be applied for image segmentation in conjunction with various region-based homogeneity terms.
- Score: 16.651587651843055
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The Voronoi diagram-based dual-front active contour models are known as a
powerful and efficient way for addressing the image segmentation and domain
partitioning problems. In the basic formulation of the dual-front models, the
evolving contours can be considered as the interfaces of adjacent Voronoi
regions. Among these dual-front models, a crucial ingredient is regarded as the
geodesic metrics by which the geodesic distances and the corresponding Voronoi
diagram can be estimated. In this paper, we introduce a type of asymmetric
quadratic metrics dual-front model. The metrics considered are built by the
integration of the image features and a vector field derived from the evolving
contours. The use of the asymmetry enhancement can reduce the risk of contour
shortcut or leakage problems especially when the initial contours are far away
from the target boundaries or the images have complicated intensity
distributions. Moreover, the proposed dual-front model can be applied for image
segmentation in conjunction with various region-based homogeneity terms. The
numerical experiments on both synthetic and real images show that the proposed
dual-front model indeed achieves encouraging results.
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