A novel stability quantification of detected interest points in scale-space

In local invariant features, the detected interest points using scale-space representations are considered the most robust to many variations in the imaging conditions. The existing approaches extract interest points at scale-space differential singularities. In these approaches, all detected interest points are considered equally robust without taking in consideration the variable sensitivities of these interest points with respect to noise effects. In this paper, we analyze the robustness of detected interest points at scale-space singularities against noise effects. Also, we propose a novel quantitative stability measure of these interest points. The evaluation results show the effectiveness of the proposed stability measure in estimating the robustness of the detected interest points to noise effects.