Corner Detection via Scale-Space Behavior-Guided Trajectory Tracing

Existing curvature scale-space (CSS) methods detect corners by tracing the CSS trajectories from a determined high scale toward the lowest one. For those images with sophisticated details, such approach could often yield unsatisfactory corner detection results; i.e., miss-detected true corners (false negatives) and round corners (false positives). In this letter, these two fundamental problems are investigated. To tackle them, a novel CSS-based corner detector is proposed by incorporating our mathematically derived scale-space properties of the planar curves and corner points into the developed trajectory tracing algorithm, called the scale-space behavior-guided trajectory tracing (SBTT). In view of lacking a benchmark dataset with the ground truth, another contribution from our work is on the establishment of an augmented test image dataset, containing 147 test images with manually-labelled ground truth and their augmented images up to 62,328 images in total. Based on the ground truth, four commonly-used metrics are exploited to conduct corner detection performance evaluation. The obtained simulation results show that our proposed corner detector yields the highest F-score, when compared with that of nine state-of-the-art methods.