Minimum-Variance Synthetic Discriminant Functions,

Abstract

The conventional synthetic discriminant functions (SDF's) determine a filter matched to a linear combination of the available training images such that the resulting cross-correlation output is constant for all training images. We remove the constraint that the filter must be matched to a linear combination of training images and consider a general solution. This general solution is, however, still a linear combination of modified training images. We investigate the effects of noise in input training images and prove that the conventional SDF's provide minimum output variance when the input noise is white. We provide the design equations for minimum-variance synthetic discriminant functions (MVSDF's) when the input noise is colored. General expressions are also provided to characterize the loss of optimality when conventional SDF's are used instead of optimal MVSDF's. Keywords: Reprints; Optical image recognition.

Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1986
Accession Number
ADA179302

Entities

People

  • Vijaya Kumar

Organizations

  • Carnegie Mellon University

Tags

DTIC Thesaurus Topics

  • Cross Correlation
  • Equations
  • Image Recognition
  • Images
  • Mathematics
  • Optical Images
  • Optical Phenomena
  • Recognition
  • Training

Readers

  • Radar Systems Engineering.
  • Regression Analysis.
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms