基于最小平均相关能量滤波器的目标识别技术

Target Recognition Technology Based on Minimum Average Correlation Energy Filters

把用于匹配滤波器的最小平均相关能量(MACE)算法进行改进,成功应用于实时联合变换相关器。训练系列图像的谱按MACE综合后,经傅里叶逆变换在物面合成参考图像,同时在合成参考图像前对训练图像进行边缘提取,并对目标与参考图像的联合功率谱进行拉普拉斯锐化,提高相关峰对比度。给出了存在旋转变化的目标图像和光学实验。实验表明,改进后的MACE方法可大大减少训练图像的数目,减小运算量,很好地解决联合变换相关器对目标存在旋转变化的识别问题,可扩大目标识别的范围,提高识别率。

Minimum average correlation energy algorithm（MACE）used in matched filter is improved and applied to real-time joint transform correlator.A set of spectram of training images are synthesized with MACE and synthesized reference images are got after inverse Fourier transform.Meanwhile,the edge extraction of training images is made before reference images synthesis,Laplace sharpening to the joint power spectrum of object image and reference image are carried out in order to enhance contrast of correlation peak.Rotation variant object images and optical experiments are showed.The experiment proves that improved MACE method can decrease training image number greatly,reduce calculation and well solve the problem of rotation variant object recognition in joint transform correlator.Target recognition scope is expanded and recognition rate is increased.