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18 January 1988 Fast Algorithm For Pattern Recognition Using Test Theory
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By applying the test theory, an object can be placed into a subspace within the hyperspace of features, which in turn gives a probability of correct classification (predictive value or diagnosability). Once the predictive value reaches or exceeds the predetermined confidence limit after a finite number of observations (tests) of the features, no additional observation is necessary. A discriminant for a given feature is set from empirical values ("experiences"), an observation of a feature needs not be a precise measure. Instead, a comparison whether the feature is greater or lesser than the discriminant can be used. Information of tests will give clues to decide the sequence of the tests in a descending order of information to classify an object with the minimum number of observa-tions. These strategies reduce the time required for observations of features and computation, and shortens the execution of pattern recognition.
© (1988) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Takashi Okagaki and T.Russell Hsing "Fast Algorithm For Pattern Recognition Using Test Theory", Proc. SPIE 0829, Applications of Digital Image Processing X, (18 January 1988);

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