Human and Ideal Observers for Detecting Image Curves

Part of Advances in Neural Information Processing Systems 16 (NIPS 2003)

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Fang Fang, Daniel Kersten, Paul R. Schrater, Alan L. Yuille


This paper compares the ability of human observers to detect target im- age curves with that of an ideal observer. The target curves are sam- pled from a generative model which specifies (probabilistically) the ge- ometry and local intensity properties of the curve. The ideal observer performs Bayesian inference on the generative model using MAP esti- mation. Varying the probability model for the curve geometry enables us investigate whether human performance is best for target curves that obey specific shape statistics, in particular those observed on natural shapes. Experiments are performed with data on both rectangular and hexagonal lattices. Our results show that human observers’ performance approaches that of the ideal observer and are, in general, closest to the ideal for con- ditions where the target curve tends to be straight or similar to natural statistics on curves. This suggests a bias of human observers towards straight curves and natural statistics.