Symmetric stability of low level feature detectors

Abstract

We investigate the capability of low level feature detectors to consistently define feature keypoints in an image and its horizontally reflected (mirrored) image. It is our assertion that this consistency is a useful attribute of a feature detector and should be considered in assessing the robustness of a feature detector. We test ten of the most popular detectors using a popular dataset of 8677 images. We define a set of error measurements to help us to understand the invariance in keypoint position, size and angle of orientation, and we use SIFT descriptors extracted from the keypoints to measure the consistency of extracted feature descriptors. We conclude that the FAST and CenSurE detectors are perfectly invariant to bilateral symmetry, Good Features to Track and the Harris Corner detector produce consistent keypoints that can be matched using feature descriptors, and others vary in their invariance. SIFT is the least invariant of all the detectors that we test.