Neural networks & visual motion perception

[jam%bu-cs.BU.EDU@bu-it.bu.edu]

The following material is available as Boston University Computer
Science Department Tech Report #88-010.  It may be obtained from
pam at bu-cs.bu.edu  or by writing to Pam Pletz, Computer Science
Dept., Boston Univ., 111 Cummington St., Boston, MA  02215, U.S.A.
It is 100 pages long, and the price is $7.00.
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		   SELF-ORGANIZING NEURAL NETWORKS
		   FOR PERCEPTION OF VISUAL MOTION

			 Jonathan A. Marshall

			       ABSTRACT

The human visual system overcomes ambiguities, collectively known as
the aperture problem, in its local measurements of the direction in
which visual objects are moving, producing unambiguous percepts of
motion.  A new approach to the aperture problem is presented, using an
adaptive neural network model.  The neural network is exposed to
moving images during a developmental period and develops its own
structure by adapting to statistical characteristics of its visual
input history.  Competitive learning rules ensure that only connection
``chains'' between cells of similar direction and velocity sensitivity
along successive spatial positions survive.  The resultant
self-organized configuration implements the type of disambiguation
necessary for solving the aperture problem and operates in accord with
direction judgments of human experimental subjects.  The system not
only accommodates its structure to long-term statistics of visual
motion, but also simultaneously uses its acquired structure to
assimilate, disambiguate, and represent visual motion events in
real-time.
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I am now at the Center for Research in Learning, Perception, and
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  --J.A.M.