Tech report available

[Eric Mjolsness]

The following technical report is now available.

------------------------------------------------------------------------------
Optimization in Model Matching and Perceptual Organization:
		      A First Look

	Eric Mjolsness, Gene Gindi, and P. Anandan

		   (YALEU/DCS/RR-634)

Abstract

We introduce an optimization approach for solving problems in computer
vision that involve multiple levels of abstraction.  Specifically, our
objective functions can include compositional hierarchies involving object-part
relationships and specialization hierarchies involving object-class
relationships.  The large class of vision problems that can be subsumed
by this method includes traditional model matching, perceptual grouping,
dense field computation (regularization), and even early feature detection
which is often formulated as a simple filtering operation.

Our approach involves casting a variety of vision problems as inexact graph
matching problems, formulating graph matching in terms of constrained
optimization, and using analog neural networks to perform the constrained
optimization.  We will show the application of this approach to shape
recognition in a domain of stick-figures and to the  perceptual grouping of
line segments into long lines.
------------------------------------------------------------------------------

available from:
connolly-eileen at yale.cs.edu

alternatively:
connolly-eileen at yale.arpa

or write to:
Eileen Connolly
Yale Computer Science Dept
51 Prospect Street
P.O. Box 2158 Yale Station
New Haven CT 06520

Please include a physical address with your request.

-------