I've been taking the Udacity Computer Vision Course, and one thing that has confused me so far is how the Generalized Hough Transform is any different from feature matching on an edge image using cross-correlation.
As I understand, the Generalized Hough Transform requires you to know the size, shape, and orientation of the object you are looking for, and using the edges of that object, parameterizes that object based on an R-table. I know it can be generalized to size and orientation, but that it is also costly to do. For now, let's say you choose not to generalize.
Given these constraints, couldn't you just do cross-correlation feature matching, using the same edge image the Hough Transform would be trained by?
What's the difference? Is there any reason Hough would perform better in this case, without generalizing to size and orientation?