A SUBSPACE APPROACH TO INVARIANT PATTERN RECOGNITION USING HOPFIELD NETWORKS
Andrew H. Gee, Sreeram V. B. Aiyer and Richard W. Prager
January 1991
This work is concerned with a pattern recognition system which uses a method of subspace projection to compare $n$-point template and unknown patterns. The system is intrinsically invariant to linear transformations, though dependent on the relative ordering of the points within the template and unknown. However, invariance to point ordering may be added through the use of a Hopfield network as an optimization tool. Finding the correct point ordering is formulated as a combinatorial optimization problem, and then mapped onto a modified Hopfield network for solution. The overall pattern recognition system is successfully used to recognize instances of the ten handwritten digits. The results confirm that the system is invariant to both linear transformations and point ordering.
If you have difficulty viewing files that end '.gz',
which are gzip compressed, then you may be able to find
tools to uncompress them at the gzip
web site.
If you have difficulty viewing files that are in PostScript, (ending
'.ps' or '.ps.gz'), then you may be able to
find tools to view them at
the gsview
web site.
We have attempted to provide automatically generated PDF copies of documents for which only PostScript versions have previously been available. These are clearly marked in the database - due to the nature of the automatic conversion process, they are likely to be badly aliased when viewed at default resolution on screen by acroread.