The Chan-Vese Model with Elastica and Landmark Constraints for Image Segmentation

In order to separate completely the objects with larger occluded boundaries in an image, we devise a new variational level set model for image segmentation combing the recently proposed Chan-Vese-Euler model with elastica and landmark constraints. For computational efficiency, we deign its Augmented Lagrangian Method(ALM) or Alternating Direction Method of Multiplier(ADMM) method by introducing some auxiliary variables, Lagrange multipliers and penalty parameters. In each loop of alternating iterative optimization, the sub-problems of minimization can be solved via simple Gauss-Seidel iterative method, or generalized soft thresholding formulas with projection methods respectively. Numerical experiments show that the proposed model not only can recover larger broken boundaries, but also can improve segmentation efficiency, decrease the dependence of segmentation on tuning parameters and initialization.