OPTICAL-PATTERN RECOGNITION USING BAYESIAN CLASSIFICATION

Two recognition systems which classify optical correlation data are de scribed and experimentally compared. Both require a priori estimates o f multivariate distributions generated from their training images (TIs ). One system uses a quadratic classifier to partition the signal spac e into regions associated with single TIs and then defines object regi ons by forming a union of the appropriate TI regions. The other system uses a composite Bayes' classifier to partition the signal space dire ctly into regions associated with single objects. Accordingly, it requ ires object class distributions which it approximates with composite a lgebraic functions constructed from the TI distribution estimates. Exp erimental results demonstrate that the composite Bayes' classifier con sistently outperforms the modified quadratic classifier, albeit margin ally, when non-TIs are processed.