2 citations · 5 across the 12 of their papers we have counts for
5 papers · 1 filter
A Unified Variational Framework for Deep Weakly Supervised Image Segmentation
Yin King Chu, Lingfeng Li, Sung Ha Kang +2
We propose a unified variational framework for image segmentation under sparse pixel-level supervision. Our method is based on a simplex-constrained Potts model with a smooth perim…
Computing Smooth Geodesics under Two-Sided Curvature Bounds with Applications to Robotics and Image Analysis
Da Chen, Zhenjiang Li, Jean-Marie Mirebeau +4
Curvature of planar curves serves as a key regularization term for computing second-order minimal paths, due to its tight relevance to desirable geometric properties such as smooth…
Neural Flow Operators can Approximate any Operator: Abstract Frameworks and Universal Approximations
Shuang Chen, Juncai He, Xue-Cheng Tai
We introduce an abstract neural flow framework for neural networks and neural operators. The framework contains two continuous-depth models, namely neural flows with composition an…
Functional Analysis and Parallel Domain Decomposition for the TV-Stokes Model
Andreas Langer, Marc Runft, Talal Rahman +2
The TV-Stokes model is a two-step variational method for image denoising that combines the estimation of a divergence-free tangent field with total variation regularization in the…
Topology-Guaranteed Image Segmentation: Enforcing Connectivity, Genus, and Width Constraints
Wenxiao Li, Xue-Cheng Tai, Jun Liu
Existing research highlights the crucial role of topological priors in image segmentation, particularly in preserving essential structures such as connectivity and genus. Accuratel…