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Nearly all biological visual systems have the ability to separate relevant signals from background clutter. The natural-signals hypothesis suggests that biological systems exploit regularities in the statistical structure of natural scenes to solve this problem. Here, we study optimal detection of target signals that occlude part of the natural backgrounds they are presented on. Occluding targets are the most common in natural vision, but most of the existing literature has focused on additive targets because they are easier to work with both mathematically and experimentally. We develop a Bayes optimal detector for occluding targets that is limited by only the approximate sampling density of the primate retina and the natural scene statistics after retinal sampling. The performance of the optimal model is then compared to data measured in a human psychophysical detection task. To represent the scene statistics used by the …