[ont.events] Feb. 23rd: Jan Mulder: "Representing ambiguity in visual knowledge"

phyllis@utcsrgv.UUCP (Phyllis Eve Bregman) (02/17/84)

      UofT Department of Computer Science Seminar Schedule for
                the week of February 20th, 1984
		
Thursday, February 23th, 3:00 P.M., GB244:  Mr. Jan Mulder, Laboratory
   for Computational Vision, Department of Computer Science, University
   of British Columbia:  "Representing ambiguity in visual knowledge".

   ABSTRACT:  One of the key issues in Computational Vision is the
   proper mapping of image features to interpretations.  Local image
   features are usually ambiguous in this respect.  Only by considering
   features over a large area can we constrain the set of possible
   interpretations for each feature.  Most Computational Vision systems
   solve this ambiguity problem by a hypothesize-and-test approach.
   A different approach is taken in the model-based system described
   in this talk.

   Multi-rooted discrimination trees are used to represent classes of
   objects that can have a similar appearance in the image.  At the
   leaves of such trees we represent basic objects which describe the
   image unambiguously.  All other nodes represent classes of such
   objects.  Rather than invoking basic objects directly, as is done
   in the hypothesize-and-test approach, we invoke the class that
   represents the complete set of possible objects.  Following the
   principle of least commitment, we then replace this class by one
   of its subclasses as we expand our focus of attention over the image.
   This process continues until the image no longer provides information
   by means of which we can do further sub-classification.

   The process of discrimination can be treated as a constraint
   satisfaction problem.  A new form of Arc Consistency has been
   developed to deal with the hierarchical domain inherent in
   discrimination trees.
-- 
		Phyllis Eve Bregman
		CSRG, Univ. of Toronto
		{decvax,linus,ihnp4,uw-beaver,allegra,utzoo}!utcsrgv!phyllis