![]() Print(np.mean(np.multiply((an(img1)),(an(img2))))/(np.std(img1)*np.std(img2)))ġ.NCC (Normalized Cross Correlation) normalized cross correlation principle and C code implementationĢ. However, the Pearson coefficient can also be used to calculate the correlation between two pictures. \colorĪlone Stand up One set Do not phase turn off , Do not phase turn off Do not One set alone Stand up。 Speaking of this, I suddenly remembered a sentence in probability: ![]() The closer the coefficient is to 1, the more relevant the two random variables, and the closer to -1, the less relevant the two random variables. The value range of Pearson's coefficient is. D(X) and D(Y) represent the variance of random variables X and Y respectively. The formula is:Īmong them, Cov(X,Y) represents the covariance of random variables X,Y. It is also the 2-dimensional version of Pearson product-moment correlation coefficient. Generally, the correlation between two random variables is judged in probability. Normalized correlation is one of the methods used for template matching, a process used for finding incidences of a pattern or object within an image. ![]() The correlation coefficient is actually Pearson's coefficient.
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