Implicit Active Model using Radial Basis Function Interpolated Level SetsXianghua Xie, Majid Mirmehdi, Implicit Active Model using Radial Basis Function Interpolated Level Sets. Proceedings of the 18th British Machine Vision Conference, pp. 1040–1049. September 2007. PDF, 5866 Kbytes.
Building on recent work by others who introduced RBFs into level sets for structural topology optimisation, we introduce the concept into active models and present a new level set formulation able to handle more complex topological changes, in particular perturbation away from the evolving front. This allows the initial contour or surface to be placed arbitrarily in the image. The proposed level set updating scheme is efficient and does not suffer from self-flattening while evolving, hence it avoids large numerical errors. Unlike conventional level set based active models, periodic re-initialisation is also no longer necessary and the computational grid can be much coarser, thus, it has great potential in modelling in high dimensional space. We show results on synthetic and real data for active modelling in 2D and 3D.