CORE: A COnfusion REduction Algorithm for Keypoints Filtering

摘要

In computer vision, extracting keypoints and computing associated features is the first step for many applications such as object recognition, image indexation, super-resolution or stereo-vision. In many cases, in order to achieve good results, pre or post-processing are almost mandatory steps. In this paper, we propose a generic pre-filtering method for floating point based descriptors which address the confusion problem due to repetitive patterns. We sort keypoints by their unicity without taking into account any visual element but the feature vectors's statistical properties thanks to a kernel density estimation approach. Even if highly reduced in number, results show that keypoints subsets extracted are still relevant and our algorithm can be combined with classical post-processing methods.