Explain difference between opencv's template matching methods in non-mathematical way

Question

I'm trying to use opencv to find some template in images. While opencv has several template matching methods, I have big trouble to understand the difference and when to use which by looking at their mathematic equization:

• CV_TM_SQDIFF
• CV_TM_SQDIFF_NORMED
• CV_TM_CCORR
• CV_TM_CCORR_NORMED
• CV_TM_CCOEFF

Can someone explain the major difference between all these method in a non-mathematical way?

Answer 1

The general idea of template matching is to give each location in the target image I, a similarity measure, or score, for the given template T. The output of this process is the image R.

Each element in R is computed from the template, which spans over the ranges of x' and y', and a window in I of the same size.

Now, you have two windows and you want to know how similar they are:

CV_TM_SQDIFF - Sum of Square Differences (or SSD):

Simple euclidian distance (squared):

• Take every pair of pixels and subtract
• Square the difference
• Sum all the squares

CV_TM_SQDIFF_NORMED - SSD Normed

This is rarely used in practice, but the normalization part is similar in the next methods.

The nominator term is same as above, but divided by a factor, computed from the - square root of the product of:

• sum of the template, squared
• sum of the image window, squared

CV_TM_CCORR - Cross Correlation

Basically, this is a dot product:

• Take every pair of pixels and multiply
• Sum all products

CV_TM_CCOEFF - Cross Coefficient

Similar to Cross Correlation, but normalized with their Covariances (which I find hard to explain without math. But I would refer to mathworld or mathworks for some examples