Constraint phase optimization in minimum variance synthetic discriminant functions
- 15 January 1988
- journal article
- Published by Optica Publishing Group in Applied Optics
- Vol. 27 (2) , 409-413
- https://doi.org/10.1364/ao.27.000409
Abstract
It is shown that proper selection of constraint phases in minimum variance synthetic discriminant functions can further reduce the output variance due to input noise. It is demonstrated with the help of examples that this reduction in variance can range from being negligible to being significant. The exact amount of reduction depends on the constraint magnitudes, training images, and the noise covariance matrix.Keywords
This publication has 10 references indexed in Scilit:
- Minimum average correlation energy filtersApplied Optics, 1987
- Classification of multiclassed stochastic images buried in additive noiseJournal of the Optical Society of America A, 1987
- Minimum-variance synthetic discriminant functionsJournal of the Optical Society of America A, 1986
- Correlation synthetic discriminant functionsApplied Optics, 1986
- Construction of low noise optical correlation filtersApplied Optics, 1986
- Optical pattern recognition using circular harmonic expansionApplied Optics, 1982
- Computer recognition of 2-D patterns using generalized matched filtersApplied Optics, 1982
- Multivariant technique for multiclass pattern recognitionApplied Optics, 1980
- Optical character recognition based on nonredundant correlation measurementsApplied Optics, 1979
- Image Correlation with Geometric Distortion Part 1: Acquisition PerformanceIEEE Transactions on Aerospace and Electronic Systems, 1978