Abstract

A comparative analysis is made of visual-perception models for estimating how the probability of distinguishing a target object is affected by background noise caused by the inhomogeneity of an outdoor scene and by the objects included in it. It is shown that a model that takes into account the regularities of the perception of visual stimuli and semantic objects describes the experimental data concerning the probability of distinguishing an object in the presence of background noise more simply and with greater reliability than a model based on signal-detection theory.

Second-order motion perception in the peripheral visual field

Johannes M. Zanker
J. Opt. Soc. Am. A 14(7) 1385-1392 (1997)

Visual signal detection in structured backgrounds. II. Effects of contrast gain control, background variations, and white noise

Miguel P. Eckstein, Albert J. Ahumada, and Andrew B. Watson
J. Opt. Soc. Am. A 14(9) 2406-2419 (1997)

Three dimensional object recognition with photon counting imagery in the presence of noise

Mehdi DaneshPanah, Bahram Javidi, and Edward A. Watson
Opt. Express 18(25) 26450-26460 (2010)

Minimum-error-probability receiver for detecting a noisy target in colored background noise

Amir H. Fazlollahi, Bahram Javidi, and Peter Willett
J. Opt. Soc. Am. A 14(4) 845-852 (1997)

Predicting the probability of facial identification using a specific object model

Richard H. Vollmerhausen, Steve Moyer, Keith Krapels, Ronald G. Driggers, Jonathan G. Hixson, and Aaron L. Robinson
Appl. Opt. 47(6) 751-759 (2008)