The Chan-Vese Model with Elastica and Landmark Constraints for Image Segmentation
This addresses image segmentation challenges in computer vision, though it appears incremental as it builds on existing variational models.
The paper tackles the problem of segmenting objects with occluded boundaries by combining the Chan-Vese model with elastica and landmark constraints, resulting in improved boundary recovery and reduced parameter/initialization dependence.
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.