Is there a way to increase the quality of feature matching by using (erosion, threshold, dilation/erosion etc..) prior to feature detection?

single views of reflective objects will always play tricks on you. I would speculate that the extracted features of differently lit coins differ in some significant way, and that makes matching difficult.

you’re right to call this research. there is research on dealing with reflective objects. you might have to impose restrictions on lighting (matte box and whatnot). maybe even require specific backgrounds (textured surface, linen cloth, graph paper). or even require multiple views and do a 3d reconstruction, even if only to condense it back down to a non-reflective view of the coin.

I’d like to see feature matching and homography fail on a pair of coins that look like they should match. I haven’t seen the failure yet.

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