Descriptor-Driven Keypoint Detection

Abstract

A methodology is proposed (and illustrated on exemplary cases) for detecting keypoints in such a way that usability of those keypoints in image matching tasks can be potentially maximized. Following the approach used for MSER detection, we localize keypoints at image patches for which the selected keypoint descriptor is maximally stable under fluctuations of the parameter(s) (e.g. image threshold, scale, shift, etc.) determining how configurations of those patches evolve. In this way, keypoint descriptors are used in the scenarios where descriptors' volatility due to minor image distortions is minimized and, thus, performances of keypoint matching are prospectively maximized. Experimental verification on selected types of keypoint descriptors fully confirmed this hypothesis. Additionally, a novel concept of semi-dense feature representation of images (based on the proposed methodology) has been preliminarily discussed and illustrated (and its prospective links with deep learning and tracking applications highlighted).