【摘要】 匹配中最常见的问题之一是根据给定的准则选择最佳匹配子区 ,而选择的关键是具有唯一性和缺乏模糊性。唯一性可以通过原始图和子图相关函数峰值的幅值和个数决定。模糊性可以用相关长度或主相关峰宽度的 5 0 %来衡量。相关长度越短 ,检测正确匹配位置的可能性就越大。提出了一种根据最小相关长度来选择最佳匹配子区的方法。此分析可扩展到二维、三维或更高维 ,而且可通过快速傅里叶变换来解决离散问题。更多还原

【Abstract】 A common problem about matching is how to select the best subset according to a given criterion. The key factors of subset selection are uniqueness and lack of ambiguity. Uniqueness could be decided by the number and magnitudes of the peaks in the cross correlation function of the original and subset functions. Lack of ambiguity could be measured by the correlation length or 50 percent width of the main correlation peak. The smaller the correlation length, the greater the probability of detecting the correct matching position. In this paper, a method is presented to select the best subset according to the minimum correlation length. The analysis can be extended to the two , three and higher dimensional problems. In discrete case the problems can be solved by using the fast Fourier transform (FFT) algorithm.更多还原