The ideal-observer performance, as measured by the area under the receiver’s operating characteristic curve, is computed for six examples of signal-detection tasks. Exact values for this quantity, as well as approximations based on the signal-to-noise ratio of the log likelihood and the likelihood-generating function, are found. The noise models considered are normal, exponential, Poisson, and two-sided exponential. The signal may affect the mean or the variance in each case. It is found that the approximation from the likelihood-generating function tracks well with the exact area, whereas the log-likelihood signal-to-noise approximation can fail badly. The signal-to-noise ratio of the likelihood ratio itself is also computed for each example to demonstrate that it is not a good measure of ideal-observer performance.
© 2000 Optical Society of America
(100.2000) Image processing : Digital image processing
(100.2960) Image processing : Image analysis
(100.5010) Image processing : Pattern recognition
(110.2970) Imaging systems : Image detection systems
(110.3000) Imaging systems : Image quality assessment
Eric Clarkson and Harrison H. Barrett, "Approximations to Ideal-Observer Performance on Signal-Detection Tasks," Appl. Opt. 39, 1783-1793 (2000)