Efficient and Robust Corner Detectors Based on Second-Order Difference of Contour

As one of the most significant local features of image, corner is widely used in many computer vision tasks. Corner detection aims to achieve the highest possible detection accuracy while minimizing the computational complexity. In this letter, we first introduce a new measurement termed as second-order difference of contour (SODC), and then examine its regular distribution, which is found to provide useful information to distinguish corners from noncorners. Based on the SODC distribution characteristics, we propose two novel corner detectors to measure the response of contour points using Manhattan distance and Euclidean distance, respectively. Numerical experiments demonstrate that the Manhattan detector greatly decreases the computational complexity, while the Euclidean detector outperforms the state-of-the-art corner detectors in terms of repeatability and localization error.