APPLYING HUMAN SPATIAL VISION MODELS TO REAL-WORLD TARGET DETECTION AND IDENTIFICATION: A TEST OF THE WILSON MODEL

Human detection and identification thresholds were measured using real-world targets to test Wilson's¹ spatial vision model. The Wilson¹ model was installed on an image-processing system along with a digitized image of a B-1B aircraft target (i.e., airplane image target (AIT). The computerized version of the Wilson¹ model was used to filter the AIT and determine the magnitudes of the responses of the individual basis filters within the model to the AIT. Two targets were generated by combining the response outputs from three basis filters contained within the computer-implemented Wilson¹ model. The first filter target (FT1) was generated from the combined outputs of the three basis filters that had highest response magnitudes to the AIT. The second filter target (FT2) was created from the response outputs of three basis filters whose response magnitudes to AIT were at one-half of the overall maximum level of all of the filters contained within the computer-implemented Wilson¹ model. To obtain detection and identification thresholds for three subjects. AIT and FT1 were presented on a homogeneous photopic background and photopic backgrounds containing either static or dynantic Gaussian "white" noise. Detection and identification thresholds measured for AIT and FT2 only used the homogeneous background. The mean detection and identification thresholds for AIT and the two filter-generated targets were compared to determine if the information contained within the filters' outputs is that which is required for detection and identification of the AIT. Mean AIT and FT1 detection and identification thresholds were only statistically significantly different when measured using dynantic Gaussian noise backgrounds. Mean AIT detection and identification thresholds were statistically significantly different from mean FT2 thresholds. The results indicate that applications of Wilson's¹ model can be used to predict the spatial information contain within real-world static targets that is required for detection and identification. Dynantic noise results provided information about the temporal sampling rate of the human visual system.0