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Issue No.04 - July (1988 vol.10)
pp: 599-606
ABSTRACT
<p>A fast, compact continuous-parameter (CP) classifier, suitable for a 16-bit microprocessor, is developed for classes which consist of a prototype manifold which is a function of one or more continuous parameters. The classification method consists of approximating the manifold by a number of unit cells and assigning a test vector to the closest cell using a Euclidean distance measure. An experiment is described in which computer-generated magnetic dipole moments are used as feature vectors to classify a set of homogeneous ferrous spheroids. The CP classifier provides accurate estimates of the orientation angles of the test object with error equal to a small fraction of the design set increment (1 degrees out of 15 degrees ).</p>
INDEX TERMS
feature vector classifier; computerised pattern recognition; multiple continuous parameters; Euclidean distance; computerised pattern recognition; vectors
CITATION
J.E. McFee, Y. Das, "A Classifier for Feature Vectors Whose Prototypes are a Function of Multiple Continuous Parameters", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.10, no. 4, pp. 599-606, July 1988, doi:10.1109/34.3922
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