Patent Document ID: 5503262
Application ID: 08013708
Patent Flag: 1

Claim One:
1. Apparatus for classifying a pattern, said apparatus comprising: (i) a receiving system; (ii) a pre-processing system; (iii) a learning classification system, the learning classification system comprising a data base for the classification of a pattern, by means of the values of physical features which are supplied to said receiving system; and a service system, said receiving system and said classification system being connected in an order of enumeration substantially to form a cascade and said service system being connected to an output of said cascade; wherein: (a) said pre-processing system (a.a) comprises means for performing a pre-processing activity which transforms said values of said physical features into a plurality of local feature vectors ALC.sub.i (l) and (b) said classification system comprises means for performing a plurality of activities which access said data base and of which: (b.a) a first activity: (b.a.a) in a first testing operation for each instance in respect of said local feature vectors ALC.sub.i (l) transformed with an operator .PHI. { }, performs a comparison with a vectorial reference value Q.sub.ALCi (l); (b.a.b) communicates a result of said first testing operation to a second activity by way of a first data channel which leads from said first activity to a second activity; and (b.a.c) computes a global line feature vector AGI.sub.i from instances of each said local feature vector ALC.sub.i (l) by means of instances of an associated first vectorial estimate ALC.sub.i *(l) stored in said data base and an associated second vectorial estimate .sigma..sub.ALCi *(l) stored in said data base, whereafter, only if said first testing operation is successfully performed, (b.b) all global line feature vectors AGI.sub.i are transferred to a third activity by way of a second data channel which leads from said first activity to said third activity, (b.c) said third activity: (b.c.a) in a second testing operation for each said global line feature vector AGI.sub.i transformed with an operator .OMEGA. { } which uses a third vectorial estimate AGI.sub.i * and a fourth vectorial estimate .sigma..sub.AGIi *, performs a comparison with a further vectorial reference value Q.sub.AGIi ; (b.c.b) communicates the result of the second testing operation to said second activity by way of a third data channel which leads from said third activity to said second activity; and (b.c.c) computes a single global surface feature vector AGF from the global line feature vectors AGI.sub.i, whereafter, only if said second testing operation is successfully performed, (b.d) said global surface feature vector AGF is transferred to a fourth activity by way of a fourth data channel which leads from said third activity to said fourth activity: (b.e) said fourth activity: (b.e.a) computes the Mahalanobis distance d.sup.2 between said global surface feature vector AGF and a fifth vectorial estimate AGF* stored in said data base, by means of a covariance matrix C.sub.AGF *; (b.e.b) in a third testing operation performs a comparison between the Mahalanobis distance d.sup.2 and a reference value Q.sub.d.sup.2 ; and (b.e.c) communicates the result of the third testing operation to the second activity by way of a fifth data channel which leads from the fourth activity to said second activity; (b.f) only if all three of said first, second and third testing operations are successfully performed, said second activity (b.f.a) computes (b.f.a.a) new first estimates ALC.sub.i *(l), (b.f.a.b) new second estimates .sigma..sub.ALCi *(l), (b.f.a.c) new third estimates AGIi*, (b.f.a.d) new fourth estimates .sigma..sub.AGIi * and (b.f.a.e) a new fifth estimate AGF* and also (b.f.a.f) a new covariance matrix C.sub.AGF * and thereby (b.f.b) updates said data base and (b.f.c) also communicates to said service system the ascertained class of said test item.