COIN-O-MATIC

A fast and reliable system for automatic coin classification

Laurens van der Maaten Paul Boon

Introduction

• Existing systems for coin classification not suitable for heterogeneous coin collections

• Heterogeneous coin collections:– MUSCLE CIS benchmark dataset– Datasets with historical coins

• Classification of heterogeneous coin collections requires incorporation of visual features

Introduction

• MUSCLE CIS benchmark:– 692 coin classes with 2,070 coin faces– 5,000 coins should be processed within 8 hours– misclassifications have a high penalty– unknown coins in test set– 1 GB memory and ~20,000 training coins

• COIN-O-MATIC was developed with these properties in mind

Sample coins

The system

• Roughly, consisting of 4 subsystems:– Segmentation– Feature extraction– Classification– Verification

The system

Segmentation

• Two-stage approach– Fast approach for ‘easy’ cases– Computationally more intensive approach for

‘difficult’ cases

Segmentation

• Easy cases– Thresholding with ti=60 to remove background

– Sobel edge detection with dynamic threshold– Morphological operations– Assume upperleft pixel to be background;

perform bucket fill operation– Check whether segmentation was successful

Segmentation

• Difficult cases are the cases in which a failure of the previous method was detected– Severe blurring of the images removes

background structure– Sobel edge detection with dynamic threshold– Idem

• Two-stage approach successful for 95% of the coin images

Feature extraction

• Edge-based statistical features– Measure statistical distributions in edge map of

the coin

• Three features– Edge distance distributions– Edge angle distributions– Edge angle-distance distributions

• Latter feature used in final system

Feature extraction

• Median filtering and contrast stretching

• Edge maps are obtained by applying a Sobel edge detection with non-maxima suppression and a dynamical threshold (using Otsu’s method)

• The borders of the coin are ignored, since they are not discriminative

Edge distance distributions• Estimate the distribution

of the distances of edge pixels to the center of the coin

• Rotation invariant feature

• Can be measured on coarse-to-fine-scales

Edge angle distributions• Measure distribution of

angles of edge pixels w.r.t. the baseline

• Not rotation invariant by definition (however, the magnitude of the Fourier transform is)

• Can be measured on number of fine scales

Edge angle-distance distr.

• Incorporate both angular and distance information in the coin stamp

• We measure EADD using 2, 4, 8, and 16 distance bins and 180 angular bins

• Resulting features are 5200-dimensional

Classification

• Area preselection (7% margin, measured from image)

• Thickness preselection (25% margin)

• Both coin sides are classified seperately using a 5-nearest neighbour classifier

Classification

• If classifications are equal– Accept this classification (no verification)

• If not– Perform ranking procedure considering 15

nearest neighbours – Perform verification of classification

Verification

• Employs averaged prototypes in MUSCLE CIS dataset

• Coin images are converted to polar space

• Blurred intensity gradients are computed

• For the prototypes, this is already done off-line

Verification

Verification

• Mutual information of coin images with all corresponding prototypes computed (for all circular shifts of the prototypes)

• Maximum MI assumed to be correct

• Sum of MI-values serves as rejection value

Implementation

• In Visual C++ 2003

• Employs IPP-library for image processing and Fourier procedures

Results

Results

• Misclassifications usually caused by coins that lack contrast for successful edge-detection

• Application of PCA speeds up the system, however, slight reduction in performance (possibly due to use of Simple PCA)

Recommendations

• Improved segmentation procedure

• Speed can be improved by applying LAESA in the 5NN-classifier

• Reliability can be be improved by always applying verification procedure, and by incorporation of rotation information

• Classification performance can be improved by applying edge-enhancing filters

Questions