基于随机蕨的光电成像末端制导目标初始化

Target Initialization Based on Random Ferns during Electro-Optical Imaging Terminal Guidance

为实现光电成像末端制导中的自适应目标初始化,针对末端制导景象匹配中图像存在尺度、旋转、灰度和3D视角差异,及传统方法运算量较大的问题,基于随机蕨分类器构造了一种新的景象匹配算法。算法首先利用基准图像进行分类器训练,然后基于该分类器对实时图像进行特征匹配。为剔除误匹配特征对,对初始匹配特征对中的对应区域分别进行尺度不变特征变换(SIFT)特征描述,基于马氏距离准则进行误匹配特征对剔除。根据顺序抽样一致性算法(PROSAC)对剩余的匹配特征对估计两图像的外极几何关系,最终根据外极几何关系求得目标在实时图像中的位置和尺寸信息。仿真结果表明,该算法能够在光电成像末端制导过程中实现稳定的目标初始化,在极端条件下的稳定性优于原随机蕨分类器算法。

In order to solve the problems of image differences in scale,rotation,grayscale and 3D viewing angle,and achieve adaptive target initialization during electro-optical imaging terminal guidance,a new scene matching framework based on random Ferns classifier was constructed.The process of realizing the method includes classifier off-line training which yields fast run-time performance was performed.Candidate match regions between reference image and run-time image were found by the classifier.Scale invariant feature transform（SIFT） descriptors of corresponding regions in each candidate matches were computed and false matches feature pairs rejecting was performed based on Mahalanobis distance criterion.Epipolar geometry of the two images was estimated by applying PROSAC to the central locations of the corresponding regions in the final matches.Target location and size in run-time image were computed based on the epipolar geometry.Simulation results show that the proposed method provides robust target initialization during electro-optical imaging terminal guidance and is more stable than original Ferns methods under severe conditions.