1 January 1998 Predicting the probability of target detection in static infrared and visual scenes using the fuzzy logic approach
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Optical Engineering, 37(1), (1998). doi:10.1117/1.601849
Abstract
The probability of detection (Pd) of targets in static infrared and visually cluttered scenes is computed using the fuzzy logic approach (FLA). The FLA is presented as a robust method for the computation and prediction of the Pd of targets in cluttered scenes. The Mamdani/Assilian and Sugeno neuro-fuzzy-based models are investigated. A large set of infrared (IR) imagery and a limited set of visual imagery are used to model the relationships between several input parameters: the contrast, camouflage condition, range, aspect, width, and experimental Pd. The fuzzy and neuro-fuzzy models gave predicted Pd values that had 0.98 correlation to the experimental Pd’s. The results obtained indicate the robustness of the fuzzy-based modeling techniques and the applicability of the FLA to those types of problems having to do with the modeling of human-in-the-loop target detection in any spectral regime.
Thomas J. Meitzler, Eui Jung Sohn, Grant R. Gerhart, Harpreet Singh, Labib Arefeh, "Predicting the probability of target detection in static infrared and visual scenes using the fuzzy logic approach," Optical Engineering 37(1), (1 January 1998). http://dx.doi.org/10.1117/1.601849
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KEYWORDS
Fuzzy logic

Palladium

Visualization

Target detection

Visual process modeling

Infrared imaging

Data modeling

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