The correlation value of an image and its template was a multimodal function, so template matching can be seen as.
图像与模板的相关值是一多峰值函数，模板匹配实质上是多峰值寻优过程。
By means of the density function fitting method of weighted sum of normal kernels the optimizing problem of template matching for non-normal distribution image is solved.
并用正态核加权和密度函数拟合法解决了非正态图象模板匹配的优化问题。
A new template matching algorithm, which based on the difference of cross correlation function, is proposed.
为此本文提出一种基于互相关函数差值的模板匹配算法。
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