Laurens van der Maaten MICC-IKAT, Maastricht University

Fast and reliable coin
classification
Laurens van der Maaten
MICC-IKAT, Maastricht University

Introduction

Overview
The challenge
System overview
Results
Conclusions

The challenge
Coins (300 tons): 698 coin classes with 2,070 different faces
Classify 5,000 coins (= 10,000 coin images)
Within 8 hours on desktop machine
At least 70% correct
Misclassifications very expensive
Classification as unknown is allowed

The challenge

System overview
Four main components
Segmentation
Feature extraction
Classification
Verification

Segmentation
The extraction of the coin from its background
at the border of a coin, there is an edge
the magnitude of the derivative of the 2D image function
is relatively high at an edge
we can estimate the derivative and threshold it
this is called edge detection

Segmentation
The result of the segmentation:
In addition, we check whether the segmentation is successful

Feature extraction
Measure statistical properties of the coin image that are
typical for this coin type
The statistical properties allow us to discriminate between
coins
The discriminating feature of a coin is its stamp

Feature extraction
Measure statistical distributions of stamp pixels over coin
stamp pixels can be found by edge detection
coin border edges are ignored

Feature extraction
Edge distance distributions
estimate distribution of
distances of edge pixels
to the center of the coin
rotation invariant
measured on multiple
scales

Feature extraction
Edge angle distributions
estimate distribution of
angle of edge pixels
w.r.t. the baseline
rotation invariance
obtained by computing
magnitude of Fourier
transform

Feature extraction
Edge angle-distance distr.
incorporate both
angular and distance
information
measured using 2, 4, 8,
and 16 distance bins
and 180 angle bins

Classification
The feature vector can be considered 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

Verification
The classifier does not provide insight in the probability that
its classification is true
Because errors are expensive, misclassifications have to be
eliminated
This is done by a direct comparison of the coin images in
order to judge their similarity

Verification
The coin is compared with the prototype corresponding to the
classification
This is done for all rotations of the coin

Results
The system classifiers 81% of the coins correctly, while
misclassifying only 0.3% of the coins
The system uses approximately 2 seconds of computation
time per coin image

Conclusions
The system allows for automatic sorting of large amounts of
European coins
Future work: further reducing the amount of
misclassifications
Future work: classification of historical coins

Thanks
Questions?