Method and apparatus for classifying currency articles
View Patent ↗Articles of currency, for example coins, are validated by progressively eliminating candidate target classes in successive classification stages. A Mahalanobis distance associated with a plurality of properties is calculated over successive stages, the results at each stage being used to reduce the number of target classes, and hence the number of calculations required, in the successive stage or stages. Preliminary stages may represent Mahalanobis distance calculations for a sub-set of the measurements represented by the final Mahalanobis distance calculation. Thus, the Mahalanobis distance calculation can be started before some of the measurement parameters required for the later stages are available.
1. A method of determining whether or not an article of currency belongs to any one of a plurality of target classes, the method comprising:
(i) deriving a plurality of measurements of the article; and
(ii) using the measurements in a plurality of correlation calculations, each of said correlation calculations being associated with a respective one of said plurality of target classes, to determine the extent to which a relationship between the measurements conforms to a correlation between the measurements in a population of a respective one of said target classes, and hence whether or not said article of currency belongs to said respective target class, wherein each of said plurality of correlation calculations is an n-parameter Mahalanobis distance calculation;
wherein, for each of said plurality of correlation calculations and respective target classes:
(iii) said correlation calculation comprises a plurality of successive classification stages, each successive classification stage performing a part only of said n-parameter Mahalanobis distance calculation, at least one of said successive classification stages using a subset of n said measurements corresponding to said n parameters, and the successive classification stages being such that the sum of successive partial correlation calculations is either equal to the full n-parameter Mahalanobis distance calculation or a part of the n-parameter Mahalanobis distance calculation; and
(iv) at least one said successive classification stage is used to determine whether the article does not belong to said respective target class.
2. The method as claimed in claim 1 wherein at least one measurement used during a classification stage is a measurement which was not available at the commencement of an earlier classification stage.
3. The method as claimed in claim 1 wherein at least one classification stage uses a combination of values calculated during both that stage and a previous stage.
4. The method as claimed in claim 1 wherein each of said plurality of correlation calculations corresponds to calculation of a Mahalanobis distance.
5. The method as claimed in claim 4 wherein at least two classification stages perform respective parts of a Mahalanobis distance calculation for respective sets of measurements, and a further classification stage completes the Mahalanobis distance classification.
6. The method as claimed in claim 5 wherein the further classification stage involves the step of summing the results of said at least two classification stages with a further value in order to derive a Mahalanobis distance.
7. The method as claimed in claim 1 to validate coins.
8. The method as claimed in claim 1 to validate banknotes.
9. The method as claimed in claim 1 comprising, for at least one said correlation calculation, summing two or more successive partial correlation calculations.
10. The method as claimed in claim 1 comprising, for at least one said correlation calculation, summing all successive partial correlation calculations.
11. The method of claim 1 wherein at least one said successive classification stage is a partial Mahalanobis distance calculation involving p of said n parameters, where p is less than n and greater than or equal to 2.
12. The method of claim 11 wherein the n-parameter Mahalanobis distance calculation involves an inverse covariance matrix, and at least one said successive classification stage uses the coefficients in a pxp square with the p diagonal coefficients on the diagonal of said inverse covariance matrix.