Learning Spatio-Temporal Invariances
James V. Stone · 1994
We present a neural network model for the unsupervised learning of high order visual invariances. The model is demonstrated on the problem of estimating sub-pixel stereo disparity from a temporal sequence of unprocessed image pairs. After learning on a given image sequence, the model's ability to detect sub-pixel disparity generalises, without additional learning, to image pairs from other sequences. 1