Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Variational methods in image segmentation
Variational methods in image segmentation
SIAM Journal on Numerical Analysis
International Journal of Computer Vision
LCIS: a boundary hierarchy for detail-preserving contrast reduction
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Variational problems and partial differential equations on implicit surfaces
Journal of Computational Physics
An Active Contour Model without Edges
SCALE-SPACE '99 Proceedings of the Second International Conference on Scale-Space Theories in Computer Vision
Gradient flows and geometric active contour models
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Semi-Implicit Level Set Methods for Curvature and Surface Diffusion Motion
Journal of Scientific Computing
An Algorithm for Total Variation Minimization and Applications
Journal of Mathematical Imaging and Vision
Numerical simulation of anisotropic surface diffusion with curvature-dependent energy
Journal of Computational Physics
Fully Discrete Finite Element Approximation for Anisotropic Surface Diffusion of Graphs
SIAM Journal on Numerical Analysis
A Local Semi-Implicit Level-Set Method for Interface Motion
Journal of Scientific Computing
Higher-Order Feature-Preserving Geometric Regularization
SIAM Journal on Imaging Sciences
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In this paper we propose an active contour model for segmentation based on the Chan-Vese model. The new model can capture inherent sharp features, i.e., the sharp corners of objects, which are often smoothed by the regularization term in segmentation. Motivated by the snaked based method in [M. Droske and A. Bertozzi, SIAM J. Imaging Sci., 3 (2010), pp. 21-51] that emphasizes straight edges and corners without regard to orientation, we develop a region based method with a level set representation. The model combines the Chan-Vese model with the level set version of a higher order nonlinear term. We extend this model to multispectral images. Higher order methods can be very stiff, so we propose a splitting scheme to remove the stiffness and prove the model's stability and convergence. Finally we show numerical results on gray, color, and hyperspectral images. We can see that the model is robust to noise.