coin-o-matic a fast and reliable system for automatic coin classification laurens van der maatenpaul...
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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
The system
• Roughly, consisting of 4 subsystems:– Segmentation– Feature extraction– Classification– Verification
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
• 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
• 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