基于传感器参数和改良CPD算法的红外与可见光图像点云配准

为实现前下视红外图像与可见光图像的有效配准，提出了一种基于传感器参数和改良CPD算法的红外与可见光图像自动配准算法.首先，利用传感器的姿态和高度信息，对前下视红外图像进行几何透视校正，消除图像间的旋转和比例缩放等差异;然后，对可见光图像和校正后的红外图像提取边缘特征点，基于相似变换模型，利用改良的CPD算法对其实现精配准.实测数据验证表明，该方法能实现对红外与可见光图像的良好配准，配准精度达到1个像素左右.

Infrared and visual image point set registration based on sensor parameters and refined CPD algorithm

In order to realize the FLIR and visual image registration effectively, an automatic registration algorithm based on sensor parameters and the refined CPD algorithm was proposed. Firstly, geometric rectification based on the attitude angle and height parameters was carried out to eliminate the rotation and scale discrepancies between the FLIR and visual images. Then the edges of visual image and rectified infrared image were extracted and a refined CPD algorithm was proposed for point set registration, the similarity transformation was adopted for fine image registration. Finally, the experiments on real FLIR data show that the proposed algorithm can realize the registration of infrared and visual images effectively and the registration precision can be around one pixel.