Boyer - 2 
to 9 minutes for 512 x 512 images on a Sun SPARC-20 workstation. 
This is for edge extraction, segmentation, and subsequent organi 
zation of the edge segments into structures such as parallelograms, 
rectangles, circles, ellipses, ribbons, and triangles. Each of these 
structure hypotheses is associated with a belief measure. 
The key notion in gaining this speed is the use of the PIN for top- 
down processing in what we call the “attentive phase.” Previous 
approaches to perceptual organization have mostly been purely bot 
tom up, without any top-down knowledge base influence and there 
fore have been entirely dependent on the imperfect input data, and 
subject to significant combinatorial thrashing as noted by the Chair 
men’s question. The PIN, which acts as a sort of knowledge base, 
besides coping with the input imperfections, also allows us to in 
tegrate multiple sources of information and to form a composite 
organization hypothesis. 
Is the perceptual grouping model really useful? Is it actually implemented and 
used? 
It should come as no surprise at this point that our answers are 
YES and YES. We are using it in aerial scene analysis and in other 
application domains. In particular, we are now (with Prof. Sudeep 
Sarkar, now of the University of South Florida, USA) applying these 
methods to the particular problem of change detection in aerial im 
agery. Changes in the scene structure are inferred as changes in 
the scene’s perceptual organization and can be detected with some 
simple statistical tests. That is, the fact that significant structural 
changes in the area have occurred can be detected. Analysis of those 
changes is, of course, more involved. Changes in the perceptual or 
ganization of the scene and, especially, its statistics, are relatively 
unaffected by lighting, weather, and viewpoint. 
More generally, we argue that the evolution of perceptual organi 
zation in biological vision, and its necessity in advanced computer 
vision systems, stems from the characteristic that perception is an 
intelligent process. This is particularly so for higher order organisms 
and, analogically, for more sophisticated computational models. We 
believe that one can represent the sophistication and performance 
of a vision system (biological or artificial) almost entirely in terms 
of its ability to compute perceptual organization.