Finding Correlation response in fourier domain Lets say I have a system that is trying to find a small image (assume all images are grayscale) within in an image by using correlation. So this system has the baseline image, and I input 5 different images (the five input images are bigger or have more data points than the baseline image) and do correlation of my baseline images against the five input images. From this I would get 5 "tables" of correlation values, and from each tables I select the highest value as the metric to how similar the input image is to the baseline image.

Now lets say I want to upgrade my system to do the correlation in the fourier domain. I pad the smaller image with zeros, do the fft, multiply the data by the conjugate of the baseline to do correlation, and I get the power spectral density at each point through some PSD estimator method, like Bartlett, welch, blackman-tukey, etc. 

From my understanding, the more spread out, or the less dense the power spectrum is over a set number of frequency bins, the less correlated the two signals are, and vice versa. 

But my question is, in the frequency domain, particularly for a two-dimensional signal, would the metric for measuring how spread the power is simply be the maximum value within the table I would attain after the correlation and PSD estimate, as in the spatial domain case?

Also if you could cite your answers whether it be a book or article, that would be much appreciated
From my understanding, the more spread out, or the less dense the power spectrum is over a set number of frequency bins, the less correlated the two signals are, and vice versa.


I'm afraid you have this backward. Narrow peaks in the original (spatial) domain correspond to strong cross-correlations. Narrow peaks in the spatial domain will be relatively spread out in the Fourier domain. The results of the cross-correlation are usually interpreted in the spatial domain (rather than the Fourier domain). 

Transform the image and the (zero-padded) template, multiply in the Fourier domain, and then transform back to the spatial domain. Peaks in the spatial domain correspond to (the possibility of) matches between the template and the image.