tech report announcement

[Mike Mozer]

Sorry to disappoint you, but this is not another request to be removed from
the mailing list.

Please do not forward this announcement to other boards.  Thank you.

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LEARNING TO SEGMENT IMAGES USING DYNAMIC FEATURE BINDING

Michael C. Mozer, Richard S. Zemel, and Marlene Behrmann

Despite the fact that complex visual scenes contain multiple, overlapping
objects, people perform object recognition with ease and accuracy.  One
operation that facilitates recognition is an early segmentation process
in which features of objects are grouped and labeled according to which object
they belong.  Current computational systems that perform this operation are
based on predefined grouping heuristics.  We describe a system called
MAGIC that _learns_ how to group features based on a set of presegmented
examples.  In many cases, MAGIC discovers grouping heuristics similar to
those previously proposed, but it also has the capability of finding
nonintuitive structural regularities in images.  Grouping is performed by a
relaxation network that attempts to dynamically bind related features.
Features transmit a complex-valued signal (amplitude and phase) to one
another; binding can thus be represented by phase locking related features.
MAGIC's training procedure is a generalization of back propagation
to complex-valued units.

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This TR has been placed in the Neuroprose archive at Ohio State.  Instructions
for its retrieval are given below.  If you are unable to retrieve and print
the TR and therefore wish to receive a hardcopy, please send mail to
conn_tech_report at cs.colorado.edu.  Please do not reply to this message.

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FTP INSTRUCTIONS

NOTE CHANGE OF HOSTNAME FROM cheops TO archive
----------------------------------------------

unix> ftp archive.cis.ohio-state.edu (or 128.146.8.52)
Name: anonymous
Password: neuron
ftp> cd pub/neuroprose
ftp> binary
ftp> get mozer.segment.ps.Z
ftp> quit
unix> zcat mozer.segment.ps.Z | lpr