An Introduction to the Design of Pattern Recognition Devices by P.W. Becker

By P.W. Becker

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Tion 14-3). The procedure is used when misclassification of C 1 -patterns is particularly undesirable. The decision proc~ dure will in practice consist of the following steps. (The pro cedure is not restricted to the familiar case, p ... l ). (0 First the likelihood ratio Lk-P(2)k/P(1)k ,k .. 1, ... ,N M,is computed for each of the NM micro-regions. : RN - P let it be W2. • • (iv) In this manner repeatedly the micro-region with the low est Lie -value is classified as a t... region. At each step it is checked that the percentage of C1~patterns that thus far has not 64 6.

This MS is applicable only in the case Nt =2 • Templet Matching 47 The designer must decide which of the seven MSs is most meaningful in this case; maybe he would prefer some other MS, the possibilities are by no means exhausted. When the designer has made his choice he can estimate the effectiveness of all the attributes of interest. £ lem of separating Nc pattern classes may be reduced to a set of problems of separating two <::lasses .. The (wnstraint Nc=2 is con sequently not very restrictive in practice.

The Selection of the Attributes 38 tortion; e. g. invariant attributes can be generated by moments (Hu 1962), autocorrelations (Mc Laughlin and Raviv 1968), integral geo-diffraction-pattern sampling (Lendaris and Stanley 1970), integral geometry (Tenery 1963) and by application of Lie group theory (Hoffmann 1965). Examples oftransformation invariant attributes may be found in papers indicated by descriptor 0 12 the in Minsky 1963. Often the patterns can be expanded using a set of orthonormal functions; in such cases the coefficients to the p most important terms of the expansion may be used as attrib utes.

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An Introduction to the Design of Pattern Recognition Devices by P.W. Becker
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