OPTICAL-PATTERN RECOGNITION USING BAYESIAN CLASSIFICATION

Citation
Gw. Carhart et al., OPTICAL-PATTERN RECOGNITION USING BAYESIAN CLASSIFICATION, Pattern recognition, 27(4), 1994, pp. 587-606
Citations number
31
Categorie Soggetti
Computer Sciences, Special Topics","Engineering, Eletrical & Electronic","Computer Science Artificial Intelligence
Journal title
ISSN journal
00313203
Volume
27
Issue
4
Year of publication
1994
Pages
587 - 606
Database
ISI
SICI code
0031-3203(1994)27:4<587:ORUBC>2.0.ZU;2-8
Abstract
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.