Multistage integration model for human egomotion perception.
Human computational vision models that attempt to account for the dynamic perception of egomotion and relative depth typically assume a common three-stage process: first, compute the optical flow field based on the dynamically changing image; second, estimate the egomotion states based on the flow; and third, estimate the relative depth/shape based on the egomotion states and possibly on a model of the viewed surface. We propose a model more in line with recent work in human vision, employing multistage integration. Here the dynamic image is first processed to generate spatial and temporal image gradients that drive a mutually interconnected state estimator and depth/shape estimator. The state estimator uses the image gradient information in combination with a depth/shape estimate of the viewed surface and an assumed model of the viewer's dynamics to generate current state estimates; in tandem, the depth/shape estimator uses the image gradient information in combination with the viewer's state estimate and assumed shape model to generate current depth/shape estimates. In this paper, we describe the model and compare model predictions with empirical data.
Zacharias
G L
GL
Charles River Analytics, Cambridge, Massachusetts 02138-1125, USA.
Miao
A X
AX
Warren
R
R
D000401:Aircraft / Q000295:instrumentation; D001359:Aviation / Q000193:education / Q000295:instrumentation; D003198:Computer Simulation; D003867:Depth Perception; D006804:Ergonomics; D005556:Form Perception; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008433:Mathematics; D008960:Models, Psychological; D009010:Monte Carlo Method; D009039:Motion Perception; D009622:Noise; D014794:Visual Fields; D014796:Visual Perception