| 摘要 |
For the classification of a pattern in particular on a banknote or a coin, a receiving system detects, by a measurement procedure, vectors of a test item, a pre-processing system transforms the measured vectors into local feature vectors ALCi(l) and a learning classification system carries out a plurality of testing operations. A first activity compares in a first testing operation each of the local feature vectors ALCi(l) to a vectorial reference value. It is only if the first testing operation takes place successfully that the first activity, by means of first estimates which are stored in a data base, links the local feature vectors ALC(l) to provide global line feature vectors AGIi. In a second testing operation a third activity compares the global line feature vectors AGIi to corresponding reference values and, if the second testing operation is successful, computes a single global surface vector AGF of which a fourth activity. in a third testing operation, compares its distance in accordance with Mahalanobis relative to an estimated surface vector to a reference value. The test item is reliably classified if all three testing operations take place successfully.
|
