Image processing, recognizing a small feature in a larger image I am trying to write an image processing program to recognize bubbles in oil.
It has been suggested I try computing the convolution of the image and an image of a typical bubble. 
i.e. : ifft(fft(a).fft(b)) will have high peaks near bubbles.

where:
ifft : inverse fourier transform 
fft : fourier transform 
a : the image 
b : kernel image (bubbles) 

Is this a good way to solve this problem? Also, for the dot product to work, I need the images to be the same dimension. This is not likely to be the case.
What has been suggested to you is called template matching. If you don't mind re-using code (and working with C++), you can find an implementation in the OpenCV library.

A simpler alternative (that I would start with) would be to work with generalized Hough transform, but that will require some pre-processing first by detecting the edges in the image.