outlier, robust statistics

[Terry Sejnowski]

I have received many requests for a reference to the motion model
I mentioned recently in the context of robust statistics.
An early version can be found in:

Nowlan, S. J. and Sejnowski, T. J., Filter selection model for generating
visual motion signals, In: C. L. Giles, S. J. Hanson and J. D. Cowan (Eds.)
Advances in Neural Information Processing Systems 5, San Mateo, CA:
Morgan Kaufman Publishers, 369-376 (1993).

Two longer papers on the computational theory and the biological
consequences are in review.

Darrell and Pentland have an interesting iterative approach
in which multiple hypotheses compete to include motion samples
within their regions of support.  A relaxation scheme must decide
on the number of objects and the correct velocity assignments.
Our approach to motion estimation is simpler in that hypotheses
do not correspond to objects, but to distinct velocities, and 
the number of hypotheses is always fixed.  This allows the
selection of regions of support to be performed non-iteratively.
The architecture of the model is feedforward with soft-max within 
layers, so it is quite fast.  Mixtures of experts was used to optimize
the weights in the network.

Terry

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