Performance estimates for maximum-likelihood pattern recognition algorithms with aberration-compensation filters

Richard B. Holmes

doi:10.1117/12.60022

1 December 1992 Performance estimates for maximum-likelihood pattern recognition algorithms with aberration-compensation filters

Richard B. Holmes

Author Affiliations +

Optical Engineering, Vol. 31, Issue 12, (December 1992). https://doi.org/10.1117/12.60022

Abstract

The probability of correct object recognition is calculated assuming a maximum-likelihood algorithm, accounting for clutter and for the opticaltransferfunction (OTF) of both the atmosphere and the imaging system. The OTF is determined by the spatial bandwidth of a real-time adaptive aberration-compensation system. The algorithm recognizes an object based on minimum distance in feature space. Each feature is associated with a corresponding scale size, which is correspondingly blurred by the OTF. Specific autocorrelation functions and probability distributions are assumed for the compensated aberrations (Gaussian) and the clutter strength (negative exponential and beta or Gaussian, respectively). The results presented assume an informationless distribution of targets in feature space and show the variation of performance with the number offeatures, the quality of compensation, and the strength of clutter.

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Richard B. Holmes "Performance estimates for maximum-likelihood pattern recognition algorithms with aberration-compensation filters," Optical Engineering 31(12), (1 December 1992). https://doi.org/10.1117/12.60022

Published: 1 December 1992

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