Learning stereo disparity using temporal smoothness constraints: a computational model.
An unsupervised learning algorithm is presented for learning stereo disparity. A key assumption is that surface depth varies smoothly over time. This assumption is consistent with a learning rule which maximizes the long-term variance of each unit's outputs, whilst simultaneously minimizing its short-term variance. The learning rule involves a linear combination of anti-Hebbian and Hebbian weight changes, over short and long time scales, respectively. The model is demonstrated on a hyperacuity task: estimating sub-pixel stereo disparity from a temporal sequence of stereograms. The algorithm generalizes, without additional learning, to previously unseen image sequences.
Stone
J V
JV
Department of Psychology, University of Sheffield, Western Bank, England. stone@aivru.sheffield.ac.uk
D000465:Algorithms; D003198:Computer Simulation; D003867:Depth Perception / Q000502:physiology*; D006801:Humans; D010468:Perceptual Disorders / Q000503:physiopathology; D015357:Vision Disparity / Q000502:physiology*