The standard way to characterize sensor performance is by means of the Minimum Resolvable Temperature Difference (MRTD) and Minimum Resolvable Contrast (MRC) methods. These methods are based on Fourier analysis and work reasonably well for linear (analogue) systems. However, nonlinear effects, such as sampling, are not properly accounted for. As an alternative, the Triangle Orientation Discrimination (TOD) method has been proposed, based on 4 oriented triangles, that can handle nonlinear effects. Here, we present a model that predicts the TOD-sensor performance characterization curve from the system parameters. It consists of i) a sensor model and ii) a model of the visual system of the observer. The sensor model generates display images which are fed into the visual system model. The visual system is modeled by a bank of band-pass filters which mimic the pattern of neural activity in the visual cortex (using a common stack model). The neural activity is calculated and internal noise is added. Finally, a decision is made based on a correlation with the expected neural activity of the 4 possible inputs. The model has been validated with two human observer experiments in which the TOD-curve 1) of the naked eye, and 2) of a simulated thermal staring sensor system were measured. An internal noise level could be found for which the TOD of the naked eye of the two observers could be predicted. The model gives reasonable (but somewhat optimistic) predictions of the TOD-sensor performance curve of the simulated staring camera. Although more tests and modifications are required, these preliminary results suggest that the model can be developed into a model which predicts the TOD for all kinds of sensor systems, which may include sampling effects, noise, blur and (local) image enhancement methods.