Human template estimation using a Gaussian Processes algorithm

摘要

In this paper we propose the use of a machine-learning algorithm based in Gaussian Processes to estimate a human observer linear template for the detection of a signal in a noisy background. Estimating a human observer template is not novel, however the use of a multi-kernel Gaussian Processes approach is. This model provides spatial smoothing by using a sparse kernel representation. For validation purposes, we train this model observer with the ground truth and the estimated template is actually the same as the statistically optimal detector. Next, we present the human observer template estimated for the detection of a signal on a different power-low background.