We present a novel level set representation and Geometric Deformable Model (GDM) evolution scheme where the analysis domain is sampled by unstructured cloud of sampling points. The points are adaptively distributed according to both local image information and level set geometry, hence allow extremely convenient enhancement/reduction of local curve precision by simply putting more/fewer points on the computation domain without grid refinement (as the cases in finite difference schemes) or remeshing (typical in finite element schemes). The GDM evolution process is then conducted on the point-sampled analysis domain, without the use of computational grid or mesh, through the precise but expensive moving least squares (MLS) approximation of the continuous domain and calculations, or the fa...[ Read more ]

We present a novel level set representation and Geometric Deformable Model (GDM) evolution scheme where the analysis domain is sampled by unstructured cloud of sampling points. The points are adaptively distributed according to both local image information and level set geometry, hence allow extremely convenient enhancement/reduction of local curve precision by simply putting more/fewer points on the computation domain without grid refinement (as the cases in finite difference schemes) or remeshing (typical in finite element schemes). The GDM evolution process is then conducted on the point-sampled analysis domain, without the use of computational grid or mesh, through the precise but expensive moving least squares (MLS) approximation of the continuous domain and calculations, or the faster yet coarser generalized finite difference (GFD) calculations. Because of the adaptive nature of the sampling point density, our strategy performs fast marching and level set local refinement concurrently. The performance of our effort is evaluated and analyzed using synthetic and real images.