Abstract
We present a vision-based approach to coin classification which is able to discriminate between hundreds of different coin classes. The approach described is a multistage procedure. In the first stage a translationally and rotationally invariant description is computed. In a second stage an illumination-invariant eigenspace is selected and probabilities for coin classes are derived for the obverse and reverse sides of each coin. In the final stage coin class probabilities for both coin sides are combined through Bayesian fusion including a rejection mechanism. Correct decision into one of the 932 different coin classes and the rejection class, i.e., correct classification or rejection, was achieved for 93.23% of coins in a test sample containing 11,949 coins. False decisions, i.e., either false classification, false rejection or false acceptance, were obtained for 6.77% of the test coins.
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