Ph.D. thesis available

[olson@cs.rochester.edu]

The following Ph.D. thesis now available:

	AN ARCHITECTURAL MODEL OF VISUAL MOTION UNDERSTANDING

	Thomas J. Olson
	Department of Computer Science
	University of Rochester

	Technical Report 305
	August 1989

Abstract:

The past few years have seen an explosion of interest in the
recovery and use of visual motion information by biological and
machine vision systems.  In the area of computer vision, a variety
of algorithms have been developed for extracting various types of
motion information from images.  Neuroscientists have made great
strides in understanding the flow of motion information from the
retina to striate and extrastriate cortex.  The psychophysics
community has gone a long way toward characterizing the limits and
structure of human motion processing.

The central claim of this thesis is that many puzzling aspects of
motion perception can be understood by assuming a particular
architecture for the human motion processing system.  The
architecture consists of three functional units or subsystems.  The
first or low-level subsystem computes simple mathematical properties
of the visual signal.  It is entirely bottom-up, and prone to error
when its implicit assumptions are violated.
The intermediate-level subsystem combines the low-level system's
output with world knowledge, segmentation information and other
inputs to construct a representation of the world in terms of primitive
forms and their trajectories.  It is claimed to be the
substrate for long-range apparent motion.
The highest level of the motion system assembles intermediate-level
form and motion primitives into scenarios that can
be used for prediction and for matching against stored models.

The lowest level of the architecture is in accord with standard models
of early motion perception, and details of the highest level are being
worked out in related thesis work by Nigel Goddard.  The secondary
contribution of this thesis is a detailed connectionist model of the
intermediate level of the architecture.  In order to compute the
trajectories of primitive shapes it is necessary to design mechanisms
for handling time and Gestalt grouping effects in connectionist networks.
Solutions to these problems are developed and used to construct a 
network that interprets continuous and apparent motion stimuli in a
limited domain.  Simulation results show that its interpretations
are in qualitative agreement with human perception.

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