Hi Tony,

Glad you figured this out. 

I'm also not a huge fan of the Matlab-style padding.  I guess
theoretically, it gives your match greater range - beyond the bounds
of the image.  In practice, I found that such behavior in the presence
of noise often gave me a peak outside the image, when I knew I wanted
my peak inside the image.

I'm mixed on the input padding.  Intuitively, I think the high value
at template center is easier to understand for a human looking at the
cross correlation result image.  I know that this confused me a lot
when I first encountered cross correlation using OpenCV.  However, any
subsequent analysis (peak finding, for example) would have to account
for the difference, and I think this would be more of a headache than
a benefit to people.  I can imagine a lot of careless analyses with
offsets of exactly half the template dimensions.

Your proposed method for the padding makes more sense to me than
Matlab's.  I would recommend defaulting to no padding, and also
recommend a little documentation that warns the user about the offset
they will have to account for if using the pad option.

I can't think of a use case for padding the output with zeros.  If
anyone else knows one, I'll be happy to learn, though.

Best,
Mike