Semantic Segmentation with Uncertainty Estimation Based on the Dirichlet Model and Anisotropic Regularization

The paper proposes a semantic segmentation method that couples Dirichlet uncertainty modeling with anisotropic regularization.

It proves Gamma-convergence and equicoercivity for the discrete energy family and reports calibrated uncertainty estimation with modest computational overhead.