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An ultimate goal of vision science is to understand performance under natural conditions. We describe a direct experimental approach for identifying and quantifying the factors that affect detection performance in natural scenes. A large collection of calibrated natural images is divided into millions of background patches that are then sorted into narrow bins along the dimensions of interest. In the present study, each bin represents a particular (narrow range of) mean luminance, contrast, and similarity (phase-independent correlation of the background to the target). Next, detection thresholds are measured parametrically for a sparse subset of bins spanning the entire space. The psychometric function for each bin is measured by randomly sampling background patches from that bin, without replacement. Finally, we analyze the residual variation of the background patches within each bin for other factors that strongly …