The mean search time of observers searching for targets in visual scenes with clutter is computed using the fuzzy logic approach (FLA). The FLA is presented as a robust method for the computation of search times and/or probabilities of detection for treated vehicles. The Mamdani/Assilian and Sugeno models have been investigated and are compared. The Search_2 dataset from TNO is used to build and validate the fuzzy logic approach for target detection modeling. The input parameters are: local luminance, range, aspect, width, and wavelet edge points, and the single output is search time. The Mamdani/Assilian model gave predicted mean search times for data not used in the training set that had a 0.957 correlation to the field search times. The data set is reduced using a clustering method, then modeled using the FLA, and results are compared to experiment.
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