Stopping Criterion for the Mean Shift Iterative Algorithm

TL;DR

This paper introduces a new stopping criterion for the mean shift iterative algorithm in image segmentation, utilizing images in Zn ring to improve segmentation quality and analyzing convergence properties.

Contribution

It proposes a novel stopping criterion based on Zn ring images and studies the equivalence classes and convergence of the algorithm.

Findings

Enhanced segmentation accuracy with the new criterion

Improved understanding of convergence behavior

Characterization of image equivalence classes

Abstract

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.