  In order to completely separate objects with large sections of occluded
boundaries in an image, we devise a new variational level set model for image
segmentation combining the Chan-Vese model with elastica and landmark
constraints. For computational efficiency, we design 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 easily solved via the Gauss-Seidel
iterative method and generalized soft thresholding formulas with projection,
respectively. Numerical experiments show that the proposed model can not only
recover larger broken boundaries but can also improve segmentation efficiency,
as well as decrease the dependence of segmentation on parameter tuning and
initialization.
