Stopping Criterion for the Mean Shift Iterative Algorithm
This work addresses image segmentation for computer vision applications, but it appears incremental as it modifies an existing algorithm's stopping criterion.
The authors tackled the problem of improving image segmentation by proposing a new stopping criterion for the mean shift iterative algorithm using images defined in Zn rings, resulting in a better segmentation as analyzed through convergence studies.
Image segmentation is a critical step in computer vision tasks constituting an essential issue for pattern recognition and visual interpretation. In this paper, we propose a new stopping criterion for the mean shift iterative algorithm by using images defined in Zn ring, with the goal of reaching a better segmentation. We carried out also a study on the weak and strong of equivalence classes between two images. An analysis on the convergence with this new stopping criterion is carried out too.