Seeing Things in Random-Dot Videos

The human visual system correctly groups features and interprets videos displaying non persistent and noisy random-dot data induced by imaging natural dynamic scenes. Remarkably, this happens even if perception completely fails when the same information is presented frame by frame. We study this property of surprising dynamic perception with the first goal of proposing a new detection and spatio-temporal grouping algorithm for such signals when, per frame, the information on objects is both random and sparse. The striking similarity in performance of the algorithm to the perception by human observers, as witnessed by a series of psychophysical experiments that were performed, leads us to see in it a simple computational Gestalt model of human perception based on temporal integration and statistical tests of unlikeliness, the a contrario framework.