I want to make a programm that can detect the differences between two similar images of an automotive fusebox. The fusebox will be placed inside a jig and so will usually will be in the same position in all the photos taken (won't be rotated, scaled or shifted. There might be subtle differences in lighting throughout the day). Here's an example of a fusebox:
Here's the application: the system will 'learn' a good image and then compare all subsequent fusebox images and check against the good image weather or not this the fusebox was assembled properly. For instance, if a 20A fuse is not placed where it should be, the fusebox is not good.
The fuses themselves are colour coded, blue is always 15A and red is always 10A etc. The fuses we use don't have clear markings on them as shown in the above picture so I can't rely on that, so this leaves character recognition out.
The question now becomes, how do I compare? Should I use histogram based image similarity tests? Would a simple subtraction of the image-under-test from the reference image suffice?