基于CAO的海域监控场景红外与可见光图像配准OA

Infrared and visible light image registration in maritime surveillance is a highly challenging task.To address the challenges of low image contrast and diverse viewing angles in this scenario,this study has improved the contour angle orientation(CAO)registration algorithm and proposed the CAO-marine surveillance scenarios(CAO-MSS)registration algorithm for maritime surveillance.The improvements include two key inno-vations:introducing a feature enhancement module for maritime scenarios based on contrast limited adaptive histogram equalization(CLAHE)in the feature point extraction stage,and designing an adaptive tilt angle error threshold screening strategy based on the cumulative distribution function(CDF)in the coarse matching stage.To evaluate the CAO-MSS algorithm,this paper has constructed a dataset of infrared and visible light image pairs using real maritime surveillance data.Experiments on this dataset show that the CAO-MSS algorithm can obtain more and more accurate feature point matches,and the mosaic images demonstrate that the connection between infrared and visible light image blocks is more coherent and natural.In the quantitative analysis,com-pared with the original CAO algorithm,the average root mean square error(RMSE)of the CAO-MSS algorithm has decreased by 46.26%,and the average precision has increased by 15.4%.