We present an automatic coin classifier mainly depending on visual features. Our multistage system starts out by segmentation using circular Hough transform, features extraction by two complementary cues and finally classification by simple nearest neighbor measure. Our features extraction process relies on rotation invariant edge orientation followed by Gabor wavelet convolution. Testing on the publicly available portion of a benchmark European coins database, we can correctly classify 93.5% and 98% of the coins using single face and double faces images respectively. We also show that our correct classification rate can reach 99.8% when adding the coin thickness measurement (which is available for this database).
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