Boyer -1 
1 
The Role of Perceptual Organization 
in 
Automated Scene Analysis 
Kim L. Boyer 
Signal Analysis and Machine Perception Laboratory 
Department of Electrical Engineering 
The Ohio State University, Columbus, Ohio, U.S.A. 
Email: kim@ee.eng.ohio-state.edu 
Abstract 
In this brief position paper, we highlight the role of perceptual organization in 
automated scene analysis and argue that it is, indeed, the sophistication of a vision 
system’s perceptual organization capability that underlies the sophistication and 
performance of the system’s performance as a whole. No other single aspect of the 
system design is apt to be so critical to its success. We will address these issues 
by considering the issues raised by-the Working Group Chairmen. Thus, this paper 
will dispense with the traditional organizational structure and simply consider the 
questions 1 . The questions as phrased by the Chairmen appear in italics , while our 
responses appear in plain type. More detail regarding this work can be found in the 
list of related publications given at the end of the paper. 
The Questions 
• Matching and grouping are fundamental processes in many tasks in computer 
vision, sensor orientation, and scene registration. In sophisticated analysis 
systems working with imagery of realistic size, matching and grouping of prim 
itives often dominates processing time. Are there any new ideas, particularly 
from photogrammetry, to improve the combinatorics? 
We cannot speak for the photogrammetrists, but we do submit that 
the voting and Gestalt graph methods accompanied by the Bayesian 
Perceptual Inference Networks (PINs)as described in our prior work 
and highlighted in the talk offer considerable relief from the combi 
natorial burden. For instance, typical execution times range from 7 
1- \Ve respond only to those questions in which we have reasonable expertise.