A quasi-concavity formulation turns global convexity into local differentiable inequalities on a segmentation mask and its derivatives, yielding a convolutional loss that unifies prior convex shape models.
Convex object (s) characterization and segmentation using level set function.Journal of Mathematical Imaging and Vi- sion, 64(1):68–88
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D-Convexity: A Unified Differentiable Convex Shape Prior via Quasi-Concavity for Data-driven Image Segmentation
A quasi-concavity formulation turns global convexity into local differentiable inequalities on a segmentation mask and its derivatives, yielding a convolutional loss that unifies prior convex shape models.