Level Line Guided Interest Point Detection
Xinyu Lin, Yingjie Zhou, Yipeng Liu, Ce Zhu · IEEE Signal Processing Letters · 2023
Detection of interest points,e.g., corners and blobs, lays the foundation for many vision tasks. Numerous methods have been proposed to improve the detection performance, and the gradient-based ones are the most investigated. However, existing gradient-based methods lack an adequate utilization of gradient orientations. In this letter, we show that the level line,i.e., the unit vector orthogonal to the gradient orientation of a specific point, is particularly important to interest point detection. The support level lines of an interest point,i.e., the level lines used to identify corners/blobs, exhibit a significantly different pattern from those of other points. Based on this observation, this letter proposes two robust interest point detectors for finding corners and blobs, respectively. For each detector, a specific type of level line difference is defined, and the corresponding differences are leveraged with different weights. Numerical experiments show the superior performance of the proposed detectors. The code will be publicly available athttps://github.com/roylin1229/LLD-IP.