Corner Detection via Scale-Space Behavior-Guided Trajectory Tracing

Abstract

Existing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">curvature scale-space</i> (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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">true</i> corners (false negatives) and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">round</i> 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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">scale-space behavior-guided trajectory tracing</i> (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.