Ensemble-Based Graph Model and Dense Reconstruction Error for Infrared Target Detection
Zhao Yunfei, Zhang Baohua, Doudou Jiao · 2020
In this paper, Ensemble-Based Graph Model and Dense Reconstruction Error for Infrared Target Detection algorithm is proposed. Firstly, the infrared image is constructed as a closed-loop by using the super-pixel segmentation. Then the saliency map of the target area and the corresponding part of the background are extracted respectively by using the graph model-based manifold ranking algorithm and the dense reconstruction error. And the fused result of the two saliency maps is insensitive to the background clutter interference, which can clearly locate the target area. The experimental results show that the proposed algorithm can suppress the background clutter interference and maintain the integrity of the target edge.