Keywords
motion perception, aperture problem, binocular vision, Bayesian model
Abstract
The human visual system encodes monocular motion and binocular disparity input before it is integrated into a single 3D percept. Here we propose a geometric-statistical model of human 3D motion perception that solves the aperture problem in 3D by assuming that (i) velocity constraints arise from inverse projection of local 2D velocity constraints in a binocular viewing geometry, (ii) noise from monocular motion and binocular disparity processing is independent, and (iii) slower motions are more likely to occur than faster ones. In two experiments we found that instantiation of this Bayesian model can explain perceived 3D line motion direction under ambiguity and uncertainty.
Start Date
14-5-2015 10:40 AM
End Date
14-5-2015 11:05 AM
Session Number
03
Session Title
Binocular Vision and Stereo
Included in
Applied Statistics Commons, Cognition and Perception Commons, Computational Neuroscience Commons, Quantitative Psychology Commons, Statistical Models Commons
Binocular 3D Motion Perception as Bayesian Inference
The human visual system encodes monocular motion and binocular disparity input before it is integrated into a single 3D percept. Here we propose a geometric-statistical model of human 3D motion perception that solves the aperture problem in 3D by assuming that (i) velocity constraints arise from inverse projection of local 2D velocity constraints in a binocular viewing geometry, (ii) noise from monocular motion and binocular disparity processing is independent, and (iii) slower motions are more likely to occur than faster ones. In two experiments we found that instantiation of this Bayesian model can explain perceived 3D line motion direction under ambiguity and uncertainty.