Normalized cross correlation via FHT - how can I get correlation score?

I'm using the 2D Fast Hartley Transform to do fast correlation of two images in the frequency domain, which is the equivalent of NCC (normalized cross correlation) in the spatial domain.

However, with NCC, I can get a confidence metric that gives me an idea of how strong the correlation is at a certain offset. In the frequency domain version, I end up with a peak-finding problem in the inverse FHT after doing the correlation, so my question is:

Can I use the value of the peak that I find in the correlation image to derive the same (or similar) confidence metric that I can get from NCC? If so, how do I calculate it?

• Can you give more info, perhaps formula for confidence metric? After inverse FHT is the result different to NCC result? Mar 15 '13 at 9:21
• NCC in the spatial domain gives a value between -1 and +1. The positive values are effectively a percentage confidence score. The correlation image that I get after inverse FHT has a peak with e.g. a very large value - I assume some combination of original pixel values that I can't easily relate to the -1..+1 equivalent. Mar 15 '13 at 9:28
• I'm thinking is that the NCC via FHT has a problem. Can you post the code for your implementation? Mar 15 '13 at 16:54
• No, the NCC via FHT is fine, I can recover the translation shift with sub-pixel precision. The only problem I have is that I need to do a spatial NCC using the recovered shift to get the confidence. I just want to do this faster, so I want to use the peak value in the correlation image to calculate the same score that NCC gives me. Mar 15 '13 at 20:20