Gradient-based analysis of non-Fourier motion

A gradient-based image analysis technique is applied to a class of non-Fourier stimuli. To create the stimuli, n translating sine waves with identical spatial and temporal frequencies, but separated by 2pi/n radians, are spatially randomly sampled to produce a P(n) stimulus. For n>or=2, the stimuli are non-Fourier. Local image gradients are represented in the form of a gradient plot, a histogram which shows the frequency of ranges of temporal gradient/spatial gradient pairs occurring. It is shown that the gradient plots contain features, oriented in gradient space, which indicate correct non-Fourier velocity. As n increases, so too does the complexity of the gradient plots, a finding which may account for the concomitant decrease in perceived coherent motion [Vision Res 37 (1997) 1459]. This paper demonstrates that the gradient plot and associated velocity plots are a useful way of assessing gradient-based motion information. Compared to the traditional Fourier based approach, gradient-based analysis can lead to different judgement of the motion information available to standard models of low-level motion processing.