# Is it possible to normalize an un-normalized cross-correlation vector after computation

I have a C++ code that computes cross-correlation for a given 2 vectors as input. It doesn't have an implicit normalization or normalized implementation of cross-correlation. But I would prefer a noramlized output. I referred the Wikipedia article on cross-correlation and it states the following formula to get a normalized cross-correlation:

I was wondering:

1. since $$\sigma_f$$ and $$\sigma_t$$ and are going to be a constant scalar for any given vectors, can I just multiply $$\frac{1}{n * \sigma_f * \sigma_t}$$

to the cross-correlation vector and obtain a normalized cross-correlation vector? Please correct me if I'm wrong here and suggest a way to do it if it's possible.

2. what can be done if both input vectors are not of the same size? Is zero-padding the shorter vector the only option? Or can the above factor be modified to accomodate both the lengths?