Laurens van der Maaten MICC-IKAT, Maastricht University

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Classification The feature vector can be consi<strong>der</strong>ed as a point in a high- dimensional space Points that are close together in the high-dimensional space, are likely to have the same class Classifiers employ this property to construct decision lines (i.e. lines that discriminate between coin types)
Classification Our classifier is trained on a trainingset of around 20,000 coin images When an unlabelled coin image is input into the system, the classifier predicts the most likely class of the coin image The classifications of both coin sides are combined
Page 1 and 2: Fast and reliable coin classificati
Page 3 and 4: Overview The challenge System overv
Page 5 and 6: The challenge
Page 7 and 8: Segmentation The extraction of the
Page 9 and 10: Feature extraction Measure statisti
Page 11 and 12: Feature extraction Edge distance di
Page 13: Feature extraction Edge angle-dista
Page 17 and 18: Verification The coin is compared w
Page 19 and 20: Conclusions The system allows for a

Laurens van der Maaten MICC-IKAT, Maastricht University

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