Segmentation and Restoration of Images on Surfaces by Parametric Active Contours with Topology Changes
This work addresses image processing on surfaces, which is incremental as it adapts existing active contour methods to a new domain.
The authors tackled the problem of segmenting and restoring images on 2D surfaces by extending active contour models to surfaces and using parametric curves, with a postprocessing diffusion step for restoration, and demonstrated the method on artificial and real images.
In this article, a new method for segmentation and restoration of images on two-dimensional surfaces is given. Active contour models for image segmentation are extended to images on surfaces. The evolving curves on the surfaces are mathematically described using a parametric approach. For image restoration, a diffusion equation with Neumann boundary conditions is solved in a postprocessing step in the individual regions. Numerical schemes are presented which allow to efficiently compute segmentations and denoised versions of images on surfaces. Also topology changes of the evolving curves are detected and performed using a fast sub-routine. Finally, several experiments are presented where the developed methods are applied on different artificial and real images defined on different surfaces.