2 citations · 4 across the 7 of their papers we have counts for
14 papers
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…
Mathematical Modeling and Convergence Analysis of Deep Neural Networks with Dense Layer Connectivities in Deep Learning
Jinshu Huang, Haibin Su, Xue-Cheng Tai +1
In deep learning, dense layer connectivity has become a key design principle in deep neural networks (DNNs), enabling efficient information flow and strong performance across a ran…