The current DoD target acquisition models have two primary deficiencies: they use simplistic representations of the vehicle and background signatures, and a highly simplified description of the human observer. The current signature representation often fails for complex signature configurations, yields inaccurate detectability and marginal pay-off predictions for low signature vehicles, is not extensible to false alarms and temporal cues, and precludes vehicle design guidance and diagnosis. The current human observer model is simplified to the same degree as the signature representation, and as such is not extensible to high fidelity signature representations. In answer to the noted deficiencies, we have developed the TARDEC visual model (TVM). We have adopted an alternative approach that is based on emerging academic computational vision models (CVM). Our approach is tailored to visual signatures, though the model is applicable to thermal, SAR as well as other categories of imagery. Color imagery, input to the model, is initially transformed into a 3D color-opponent space comprising luminance, red-green, and yellow- blue axes. Each plane in the color-opponent space is then decomposed by local, oriented spatial frequency analyzers (Gabor or wavelet filters) in keeping with current knowledge of retinal/cortical processing. Signal-to-noise statistics are then calculated on each channel, appropriately aggregated over all channels, and used within the signal detection theory context to predict detection and false alarm probabilities.