The grey level profiles of adjacent image regions tend to be different, whilst the `hidden' physical parameters associated with these regions (e.g. surface depth, edge orientation) tend to have similar values. We demonstrate that a network in which adjacent units receive inputs from adjacent image regions learns to code for hidden parameters. The learning rule takes advantage of the spatial smoothness of physical parameters in general to discover particular parameters embedded in grey level profiles which vary rapidly across an input image. We provide examples in which networks discover stereo disparity and feature-orientation as invariances underlying image data.
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