research reports available

[Richard Zemel]

The following two technical reports are now available.  The first report
describes the main ideas of TRAFFIC.  It appeared in the Proceedings of
the 1988 Connectionist Summer School, Morgan Kaufmann Publishers, edited 
by D.S. Touretzky, G.E. Hinton, and T.J. Sejnowski.

The second report is a revised version of my Master's thesis.  It
contains a thorough description of the model, as well as implementation
details and some experimental results.  This report is rather
long (~75 pages), so if you are curious about the model we'll
send you the first one.  On the other hand, if you want to plough
through the details, ask specifically for the second one.


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	      "TRAFFIC: A Model of Object Recognition 
		   Based On Transformations of Feature Instances"
				       
	      Richard S. Zemel, Michael C. Mozer, Geoffrey E. Hinton
			Department of Computer Science
			    University of Toronto
				       
		 Technical report CRG-TR-88-7 (Sept. 1988)

				   ABSTRACT

Visual object recognition involves not only detecting the presence of
salient features of objects, but ensuring that these features are in 
the appropriate relationships to one another.  Recent connectionist models 
designed to recognize two-dimensional shapes independent of their
orientation, position, and scale have primarily dealt with simple objects,
and they have not represented structural relations of these objects
in an efficient manner.  A new model is proposed that takes advantage
of the fact that given a rigid object, and a particular feature of that
object, there is a fixed viewpoint-independent tranformation from the 
feature's reference frame to the object's.  This fixed transformation
can be expressed as a matrix multiplication that is efficiently
implemented by a set of weights in a connectionist network.  By using
a hierarchy of these transformations, with increasing feature complexity in
each successive layer, a network can recognize multiple objects in parallel.


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	    "TRAFFIC: A Connectionist Model of Object Recognition"
				       
			      Richard S. Zemel
			Department of Computer Science
			    University of Toronto
				       
		 Technical report CRG-TR-89-2  (March 1989)

				   ABSTRACT

Recent connectionist models designed to recognize two-dimensional shapes
independent of their orientation, position, and scale have not represented
structural relations of the objects in an efficient manner.  A new model
is described that takes advantage of the fact that given a rigid object,
and a particular feature of that object, there is a fixed 
viewpoint-independent transformation from the feature's reference frame
to the object's.  This fixed transformation can be expressed as a
matrix multiplication that is efficiently implemented by a set of
weights in a connectionist network.  The model, called TRAFFIC (a 
loose acronym for ``transforming feature instances''), uses a
hierarchy of these transformations, with increasing feature complexity
in each successive layer, in order to recognize multiple objects in 
parallel.  An implementation of TRAFFIC is described, along with
experimental results demonstrating the network's ability to recognize
constellations of stars in a viewpoint-independent manner.


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Copies of either report can be obtained by sending an email request to:
    INTERNET: carol at ai.toronto.edu
    UUCP: uunet!utai!carol
    BITNET: carol at utorgpu