A High-Order Scheme for Image Segmentation via a modified Level-Set method
This work addresses image segmentation for computer vision applications, but it is incremental as it extends a previously introduced 1D filtered scheme to 2D with modifications.
The authors tackled the problem of image segmentation by proposing a high-order accurate scheme based on a modified level-set method, which introduces a new velocity for curve evolution to improve stability with minimal additional cost, and numerical tests on synthetic and real images confirmed its accuracy and advantages.
In this paper we propose a high-order accurate scheme for image segmentation based on the level-set method. In this approach, the curve evolution is described as the 0-level set of a representation function but we modify the velocity that drives the curve to the boundary of the object in order to obtain a new velocity with additional properties that are extremely useful to develop a more stable high-order approximation with a small additional cost. The approximation scheme proposed here is the first 2D version of an adaptive "filtered" scheme recently introduced and analyzed by the authors in 1D. This approach is interesting since the implementation of the filtered scheme is rather efficient and easy. The scheme combines two building blocks (a monotone scheme and a high-order scheme) via a filter function and smoothness indicators that allow to detect the regularity of the approximate solution adapting the scheme in an automatic way. Some numerical tests on synthetic and real images confirm the accuracy of the proposed method and the advantages given by the new velocity.