30 August 2002 Infrared imaging system performance model based on machine vision
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Infrared imaging system performance models (IRISPM) have become increasingly important in providing performance estimates for developmental infrared systems. Many factors such as the target and background signature, atmospheric attenuation, sensor response, image processing algorithms contribute to detection performance. For accurate evaluation of the IR system performance, all these factors should be considered. Traditionally, IRISPM utilize the minimum resolvable temperature difference (MRTD) and the Johnson criteria to predict IR imaging system field performance. However, these kinds ofperformance models are not suitable for JR imaging system with machine vision. To make up for the default of the above performance models, this paper provides a generic JR imaging system performance evaluation model based on the machine vision, and introduces system power transfer function (PTF) to describe the total influence of subsystems on the performance. The emphasis is put on the effect of background clutter and imaging processing algorithm on the detection performance. And the theoretical expression of the Signal-to-Interference (background clutter and system noise) ratio (SIR) is derived. Then, system detection range, acquisition probability and false alarm probability are acquired according to the given detection algorithm threshold.
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Xiaorui Wang, Xiaorui Wang, Jianqi Zhang, Jianqi Zhang, } "Infrared imaging system performance model based on machine vision", Proc. SPIE 4925, Electronic Imaging and Multimedia Technology III, (30 August 2002); doi: 10.1117/12.481547; https://doi.org/10.1117/12.481547

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