Computational modelling of motion detectors: responses to two-frame displays

Schemes for motion detection fall into two classes. Reichardt correlators compare spatial luminance patterns at two locations at different times; gradient detectors compare spatial and temporal luminance gradients. Both are candidate operators for biological and machine vision systems. A large body of perceptual data exists, defining the properties of motion detectors used by human observers, which can form a basis for determining which class of detector is appropriate for the human visual system. Plausible versions of each detector were implemented, and their responses to a variety of two-frame stimuli were computed. Results indicated that both detectors can predict most of the data, but on balance gradient detectors offer the best working hypothesis for motion detection by human observers. This conclusion is necessarily limited to the type of stimuli used, and may require modification in the light of responses to continuously moving stimuli.