Learning Spatio-Temporal Invariances

James V. Stone
1994 Procedings of the British Machine Vision Conference 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.
doi:10.5244/c.8.67 dblp:conf/bmvc/Stone94 fatcat:mw2jbnlpvra2fg5a6fb2cq3h6a