Adaptive Corner Detection Based on Direct Curvature Scale Space

Abstract

Corner detection based on global and local curvature properties is an advanced method for detecting corners in images, which is a fundamental composition of many algorithms. However, we find that it is time-consuming for real-time applications and might detect wrong corners or lose some important corners. To alleviate these problems, we propose an improved curvature product corner detector with dynamic region of support based on Direct Curvature Scale Space (DCSS). Firstly, we use direct curvature scale space to reduce the complexity of computation instead of curvature scale space. Secondly, multi-scale curvature product with certain threshold is used to strengthen the corner detection. Finally, we check the angles of corner candidates in the dynamic region of support in order to eliminate falsely detected corners and use an adaptive curvature threshold to remove round corners from the initial list. The experimental results show that our proposed method improves the performance of corner detection both on accuracy and efficiency, and gain more stable corners at the same time.