对于背景呈非线性变化的复杂图像,用背景预测的方法对红外点目标进行检测时,传统的线性最小二乘法(Least Squares, LS)的效果比较差。文章使用核方法(Kernel Methods, KMs)推导了最小二乘法的非线性版本:核最小二乘算法(Kernel Least Squares, KLS);进一步推导出了更适合动态系统时序预测的指数加权形式的核最小二乘算法(Kernel Exponential Weighted Least Squares, KEWLS)。提出了一种基于核方法的红外点目标检测算法,先用KEWLS 非线性回归算法预测红外图像背景,再通过自适应门限检测残差图像中的目标。非线性函数回归和红外序列图像检测实验表明核方法较大地改进了算法的非线性函数估计与红外背景预测能力。

As one of the background estimation algorithms for Infrared (IR) point target detection, Least Squares (LS) method has a poor performance to the complex nonlinear background. A nonlinear version of the least squares algorithm, called Kernel Least Squares (KLS) is deduced by using Kernel Methods (KMs). Furthermore, the exponential weighted form of KLS, called KEWLS, is deduced. KEWLS is more adaptive to dynamic nonlinear system’s time-series prediction. A kernel-based IR target detection algorithm is proposed, image background is estimated by KEWLS nonlinear regression, and then target is detected by self-adaptive threshold detection in the difference image. It is shown by nonlinear function regression and sequence IR images detection experiments that the kernel methods improve the performance of nonlinear function regression and IR background estimation.