Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Dynamic 3D Models with Local and Global Deformations: Deformable Superquadrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric partial differential equations and image analysis
Geometric partial differential equations and image analysis
Geometry and topology for mesh generation
Geometry and topology for mesh generation
Regularized Laplacian Zero Crossings as Optimal Edge Integrators
International Journal of Computer Vision
Gradient flows and geometric active contour models
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Geometric Level Set Methods in Imaging,Vision,and Graphics
Geometric Level Set Methods in Imaging,Vision,and Graphics
Computing Geodesics and Minimal Surfaces via Graph Cuts
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Efficient and reliable schemes for nonlinear diffusion filtering
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
| Hi-index | 0.00 |
Level-Set methods have been successfully applied to 2D and 3D boundary detection problems. The geodesic active contour model has been particularly successful. Several algorithms for the discretisation have been proposed and the banded approach has been used to improve efficiency, which is crucial in 3D boundary detection. In this paper we propose a new scheme to numerically represent and evolve surfaces in 3D. With the new scheme, efficiency and accuracy are further improved. For the representation, space is partitioned into tetrahedra and finite elements are used to define the level-set function. Extreme sparsity is obtained by maintaining data only for tetrahedra that contain the zero level-set. We formulate the evolution PDE in weak form and incorporate a normalisation term. We obtain a stable scheme with consistent sub-grid accuracy without having to rely on any re-initialisation procedure. Boundary detection is performed using an anisotropic extension of the isotropic geodesic model. With the sparse representation, the anisotropic model is computationally feasible. We present experimental results on volumetric data sets including images with a significant amount of noise.