An Improved Self-adapting Corner Detection Algorithm for Low-contrast Images
Yue Wang, Xiong Tang, Meng-lei Xia, Liang Sun · 2022 5th International Symposium on Autonomous Systems (ISAS) · 2022
In order to suppress the impact of image dithering caused by wind-induced vibration, the features of sequential frame should be extracted. The Harris corner detection algorithm has been widely used for feature extraction. In the field of security monitoring and control, the image and video usually have the characteristics of jumbo size, high pixel and low contrast, which are difficult to obtain the corners. For the problems above, an improved self-adapting conner detection algorithm is proposed in this paper. Firstly, some of the corners are selected according to the comparison results between target pixel and the pixels around. Secondly, the selected corners are classified by reference to adaptive threshold values. Finally, false and marginal corners can be reduced or eliminated so as to select the best-matching corners. The above improved algorithm is validated in the field of sea area security monitoring and control. Simulation results show the effectiveness and feasibility of the algorithm above.