Estimates of human-observer templates for a simple detection task in correlated noise.

An observer template encapsulates the relative weight assigned to each pixel by an observer performing a detection or dicrimination task. In previous work, Abbey et AZ. (Proc. SPIE 3663, 1999.) have developed a methodology for estimating human-observer templates in two-alternative forced-choice detection and classification experiments. The methodology assumes a Linear observer, and is designed for use on images with noise that is Gaussian distributed. In this work, the methodology is used to evaluate human-observer templates for tasks in correlated Gaussian noise. We consider detection of a Gaussian "bump" signal in image noise that has either lowpass or highpass correlation structures, as well as a white-noise task for reference. The estimated templates show that the human observers are sensitive to the correlation structure of the noise (as expected). However, the human-observer templates do not appear to fully adapt to noise correlations as the ideal observer does.