Abstract
A triangle orientation discrimination threshold(TOD) performance theoretical model is derived for the staring thermal imager based on machine vision. Specifically, how to obtain the TOD curve based on machine vision is briefly described. The spatial frequency distribution of the triangle test pattern is first determined. The transform and response characteristics of the non-periodic triangle pattern and its background clutter through machine vision-based thermal imager are analyzed. The three-dimensional matched filter is adopted to characterize quantitatively the spatial and temporal integration of image enhancement algorithms to the output triangle pattern signal, various noise components and background clutter, and the signal-to-interference ratio (SIR) of the triangle pattern output image is derived for the staring thermal imager based on machine vision. Then, the TOD performance theoretical model is established by assuming that the output SIR is equal to the threshold SIR75% determined by the discrimination criteria of machine vision. Preliminary simulation and experimental results show that this theoretical model can give reasonable prediction of the TOD performance curve for staring thermal imagers based on machine vision.
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