Motion perception and motion estimation by total-least squares

A computational model of motion perception is proposed. The model, which is gradient-based, adheres to the neural constraint that transmitted signals are positive-valued functions by posing the estimation of image motion as a quadratic programming problem combined with total-least squares: a model that assumes that image signals are contaminated by noise in both the spatial and temporal dimensions. By shrinking motion estimates with a regularizer whose subtractive effect introduces a contrast dependent speed threshold into motion computations, it is shown that the total-least squares model when posed as a quadratic programming problem, is capable of explaining both increases and decreases in perceived speed as these effects were reported by Thompson (1982) to vary as a function of image contrast and temporal frequency. The correlation that exists between the model's contrast speed response and results reported from visual psychophysics is consistent with the view that the visual system assumes that image signals may be contaminated by noise in both the spatial and the temporal domain, and that visual motion is influenced by the consequence of these assumptions.