1 July 1992 Performance estimates for maximum-likelihood pattern recognition algorithms with distortion-compensation filters
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Abstract
The probability of correct object recognition is calculated assuming a maximum-likelihood algorithm, accounting for the modulation transfer function (MTF) of the atmosphere-imaging- system combination, and for the presence of clutter. The MTF is determined by the spatial bandwidth of the adaptive distortion-compensation filter. The algorithm recognizes an object based on minimum distance in feature space. Each feature has a corresponding scale size, with a corresponding blur from the MTF. It is demonstrated that the Vander Lugt filter is a maximum-likelihood estimator for a broad range of clutter and distortion statistics. Specific probability distributions are assumed for the compensated distortions (log normal), and the clutter strength (beta and Gaussian). The results presented assume an informationless distribution of targets in feature space, and show the variation of performance with the number of features, the quality of the compensation filter, and the strength of clutter.
© (1992) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Richard B. Holmes, "Performance estimates for maximum-likelihood pattern recognition algorithms with distortion-compensation filters", Proc. SPIE 1702, Hybrid Image and Signal Processing III, (1 July 1992); doi: 10.1117/12.60554; https://doi.org/10.1117/12.60554
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