Fast image segmentation and restoration using parametric curve evolution with junctions and topology changes
This work addresses image processing tasks for applications like computer vision, but it appears incremental as it builds on existing curve evolution models with specific enhancements.
The paper tackled the problem of image segmentation and restoration by introducing a curve evolution scheme that handles junctions, vector-valued images, and topology changes, combined with denoising, resulting in a fast and efficient method as demonstrated in numerical simulations.
Curve evolution schemes for image segmentation based on a region based contour model allowing for junctions, vector-valued images and topology changes are introduced. Together with an a posteriori denoising in the segmented homogeneous regions this leads to a fast and efficient method for image segmentation and restoration. An uneven spread of mesh points is avoided by using the tangential degrees of freedom. Several numerical simulations on artificial test problems and on real images illustrate the performance of the method.